- 1.UNIT-8UNIT-8 Mining Complex Types of DataMining Complex Types
of Data LectureLecture TopicTopic
****************************************************************************************************
Lecture-50Lecture-50 Multidimensional analysis and
descriptiveMultidimensional analysis and descriptive mining of
complex data objectsmining of complex data objects
Lecture-51Lecture-51 Mining spatial databasesMining spatial
databases Lecture-52Lecture-52 Mining multimedia databasesMining
multimedia databases Lecture-53Lecture-53 Mining time-series and
sequenceMining time-series and sequence datadata
Lecture-54Lecture-54 Mining text databasesMining text databases
Lecture-55Lecture-55 Mining the World-Wide WebMining the World-Wide
Web
2. Lecture-50Lecture-50 Multidimensional analysis
andMultidimensional analysis and descriptive mining of
complexdescriptive mining of complex data objectsdata objects 3.
Mining Complex Data Objects:Mining Complex Data Objects:
Generalization of Structured DataGeneralization of Structured Data
Set-valued attributeSet-valued attribute Generalization of each
value in the set into itsGeneralization of each value in the set
into its corresponding higher-level conceptscorresponding
higher-level concepts Derivation of the general behavior of the
set, suchDerivation of the general behavior of the set, such as the
number of elements in the set, the types oras the number of
elements in the set, the types or value ranges in the set, or the
weighted average forvalue ranges in the set, or the weighted
average for numerical datanumerical data hobbyhobby = {= {tennis,
hockey, chess, violin,tennis, hockey, chess, violin,
nintendo_gamesnintendo_games} generalizes to {} generalizes to
{sports, music,sports, music, video_gamesvideo_games}} List-valued
or a sequence-valued attributeList-valued or a sequence-valued
attribute Same as set-valued attributes except that the orderSame
as set-valued attributes except that the order of the elements in
the sequence should beof the elements in the sequence should be
observed in the generalizationobserved in the generalization
Lecture-50 - Multidimensional analysis and descriptive mining of
complex data objectsLecture-50 - Multidimensional analysis and
descriptive mining of complex data objects 4. Generalizing Spatial
and Multimedia DataGeneralizing Spatial and Multimedia Data Spatial
data:Spatial data: Generalize detailed geographic points into
clustered regions,Generalize detailed geographic points into
clustered regions, such as business, residential, industrial, or
agricultural areas,such as business, residential, industrial, or
agricultural areas, according to land usageaccording to land usage
Require the merge of a set of geographic areas by spatialRequire
the merge of a set of geographic areas by spatial
operationsoperations Image data:Image data: Extracted by
aggregation and/or approximationExtracted by aggregation and/or
approximation Size, color, shape, texture, orientation, and
relative positionsSize, color, shape, texture, orientation, and
relative positions and structures of the contained objects or
regions in the imageand structures of the contained objects or
regions in the image Music data:Music data: Summarize its melody:
based on the approximate patterns thatSummarize its melody: based
on the approximate patterns that repeatedly occur in the
segmentrepeatedly occur in the segment Summarized its style: based
on its tone, tempo, or the majorSummarized its style: based on its
tone, tempo, or the major musical instruments playedmusical
instruments played Lecture-50 - Multidimensional analysis and
descriptive mining of complex data objectsLecture-50 -
Multidimensional analysis and descriptive mining of complex data
objects 5. Generalizing Object DataGeneralizing Object Data Object
identifier: generalize to the lowest level of class in theObject
identifier: generalize to the lowest level of class in the
class/subclass hierarchiesclass/subclass hierarchies Class
composition hierarchiesClass composition hierarchies generalize
nested structured datageneralize nested structured data generalize
only objects closely related in semantics to the currentgeneralize
only objects closely related in semantics to the current oneone
Construction and mining of object cubesConstruction and mining of
object cubes Extend the attribute-oriented induction methodExtend
the attribute-oriented induction method Apply a sequence of
class-based generalization operators onApply a sequence of
class-based generalization operators on different
attributesdifferent attributes Continue until getting a small
number of generalized objects thatContinue until getting a small
number of generalized objects that can be summarized as a concise
in high-level termscan be summarized as a concise in high-level
terms For efficient implementationFor efficient implementation
Examine each attribute, generalize it to simple-valued dataExamine
each attribute, generalize it to simple-valued data Construct a
multidimensional data cube (object cube)Construct a
multidimensional data cube (object cube) Problem: it is not always
desirable to generalize a set of values toProblem: it is not always
desirable to generalize a set of values to single-valued
datasingle-valued data Lecture-50 - Multidimensional analysis and
descriptive mining of complex data objectsLecture-50 -
Multidimensional analysis and descriptive mining of complex data
objects 6. An Example: Plan Mining by Divide andAn Example: Plan
Mining by Divide and ConquerConquer Plan: a variable sequence of
actionsPlan: a variable sequence of actions E.g., Travel (flight):
airline, price, seat> Plan mining: extraction of important or
significant generalizedPlan mining: extraction of important or
significant generalized (sequential) patterns from a planbase (a
large collection of plans)(sequential) patterns from a planbase (a
large collection of plans) E.g., Discover travel patterns in an air
flight database, orE.g., Discover travel patterns in an air flight
database, or find significant patterns from the sequences of
actions in thefind significant patterns from the sequences of
actions in the repair of automobilesrepair of automobiles
MethodMethod Attribute-oriented induction on sequence
dataAttribute-oriented induction on sequence data A generalized
travel plan: A generalized travel plan: Divide & conquer:Mine
characteristics for each subsequenceDivide & conquer:Mine
characteristics for each subsequence E.g., big*: same airline,
small-big: nearby regionE.g., big*: same airline, small-big: nearby
region Lecture-50 - Multidimensional analysis and descriptive
mining of complex data objectsLecture-50 - Multidimensional
analysis and descriptive mining of complex data objects 7. A Travel
Database for PlanA Travel Database for Plan MiningMining Example:
Mining a travel planbaseExample: Mining a travel planbase plan#
action# departure depart_time arrival arrival_time airline 1 1 ALB
800 JFK 900 TWA 1 2 JFK 1000 ORD 1230 UA 1 3 ORD 1300 LAX 1600 UA 1
4 LAX 1710 SAN 1800 DAL 2 1 SPI 900 ORD 950 AA . . . . . . . . . .
. . . . . . . . . . . . . . airport_code city state region
airport_size 1 1 ALB 800 1 2 JFK 1000 1 3 ORD 1300 1 4 LAX 1710 2 1
SPI 900 . . . . . . . . . . . . . . . Travel plans table Airport
info table Lecture-50 - Multidimensional analysis and descriptive
mining of complex data objectsLecture-50 - Multidimensional
analysis and descriptive mining of complex data objects 8.
Multidimensional AnalysisMultidimensional Analysis StrategyStrategy
Generalize theGeneralize the planbase inplanbase in
differentdifferent directionsdirections Look forLook for
sequentialsequential patterns in thepatterns in the
generalizedgeneralized plansplans Derive high-levelDerive
high-level plansplans A multi-D model for the planbase Lecture-50 -
Multidimensional analysis and descriptive mining of complex data
objectsLecture-50 - Multidimensional analysis and descriptive
mining of complex data objects 9. MultidimensionalMultidimensional
GeneralizationGeneralization Plan# Loc_Seq Size_Seq State_Seq 1 ALB
- JFK - ORD - LAX - SAN S - L - L - L - S N - N - I - C - C 2 SPI -
ORD - JFK - SYR S - L - L - S I - I - N - N . . . . . . . . .
Multi-D generalization of the planbase Plan# Size_Seq State_Seq
Region_Seq 1 S - L+ - S N+ - I - C+ E+ - M - P+ 2 S - L+ - S I+ -
N+ M+ - E+ . . . . . . . . . Merging consecutive, identical actions
in plans %]75[)()( ),(_),(_),,( yregionxregion
LysizeairportSxsizeairportyxflight = Lecture-50 - Multidimensional
analysis and descriptive mining of complex data objectsLecture-50 -
Multidimensional analysis and descriptive mining of complex data
objects 10. Generalization-Based Sequence
MiningGeneralization-Based Sequence Mining Generalize planbase in
multidimensional wayGeneralize planbase in multidimensional way
using dimension tablesusing dimension tables Use no of distinct
values (cardinality) at eachUse no of distinct values (cardinality)
at each level to determine the right level oflevel to determine the
right level of generalization (level-planning)generalization
(level-planning) Use operatorsUse operators mergemerge +,+,
optionoption [] to further[] to further generalize
patternsgeneralize patterns Retain patterns with significant
supportRetain patterns with significant support Lecture-50 -
Multidimensional analysis and descriptive mining of complex data
objectsLecture-50 - Multidimensional analysis and descriptive
mining of complex data objects 11. Generalized Sequence
PatternsGeneralized Sequence Patterns AirportSize-sequence survives
the min thresholdAirportSize-sequence survives the min threshold
(after applying(after applying mergemerge operator):operator):
SS--LL++ --SS [35%],[35%], LL++ --SS [30%],[30%], SS--LL++
[24.5%],[24.5%], LL++ [9%][9%] After applyingAfter applying
optionoption operator:operator: [S][S]--LL++ -[-[S]S]
[98.5%][98.5%] Most of the time, people fly via large airports to
get toMost of the time, people fly via large airports to get to
final destinationfinal destination Other plans: 1.5% of chances,
there are otherOther plans: 1.5% of chances, there are other
patterns: S-S, L-S-Lpatterns: S-S, L-S-L Lecture-50 -
Multidimensional analysis and descriptive mining of complex data
objectsLecture-50 - Multidimensional analysis and descriptive
mining of complex data objects 12. Lecture-51Lecture-51 Mining
spatial databasesMining spatial databases 13. Spatial Data
WarehousingSpatial Data Warehousing Spatial data warehouse:
Integrated, subject-oriented,Spatial data warehouse: Integrated,
subject-oriented, time-variant, and nonvolatile spatial data
repository fortime-variant, and nonvolatile spatial data repository
for data analysis and decision makingdata analysis and decision
making Spatial data integration: a big issueSpatial data
integration: a big issue Structure-specific formats (raster- vs.
vector-based,Structure-specific formats (raster- vs. vector-based,
OO vs. relational models, different storage andOO vs. relational
models, different storage and indexing)indexing) Vendor-specific
formats (ESRI, MapInfo, Integraph)Vendor-specific formats (ESRI,
MapInfo, Integraph) Spatial data cube: multidimensional spatial
databaseSpatial data cube: multidimensional spatial database Both
dimensions and measures may contain spatialBoth dimensions and
measures may contain spatial componentscomponents Lecture-51 -
Mining spatial databasesLecture-51 - Mining spatial databases 14.
Dimensions and Measures in SpatialDimensions and Measures in
Spatial Data WarehouseData Warehouse Dimension modelingDimension
modeling nonspatialnonspatial e.g. temperature: 25-30e.g.
temperature: 25-30 degrees generalizes todegrees generalizes to
hothot spatial-to-nonspatialspatial-to-nonspatial e.g. region
B.C.e.g. region B.C. generalizes togeneralizes to description
description westernwestern provincesprovinces
spatial-to-spatialspatial-to-spatial e.g. region Burnabye.g. region
Burnaby generalizes to regiongeneralizes to region Lower
MainlandLower Mainland MeasuresMeasures numericalnumerical
distributive ( count, sum)distributive ( count, sum) algebraic
(e.g. average)algebraic (e.g. average) holistic (e.g. median,
rank)holistic (e.g. median, rank) spatialspatial collection of
spatialcollection of spatial pointers (e.g. pointers topointers
(e.g. pointers to all regions with 25-30all regions with 25-30
degrees in July)degrees in July) Lecture-51 - Mining spatial
databasesLecture-51 - Mining spatial databases 15. Example: BC
weather patternExample: BC weather pattern analysisanalysis
InputInput A map with about 3,000 weather probes scattered in B.C.A
map with about 3,000 weather probes scattered in B.C. Daily data
for temperature, precipitation, wind velocity, etc.Daily data for
temperature, precipitation, wind velocity, etc. Concept hierarchies
for all attributesConcept hierarchies for all attributes
OutputOutput A map that reveals patterns: merged (similar) regionsA
map that reveals patterns: merged (similar) regions GoalsGoals
Interactive analysis (drill-down, slice, dice, pivot,
roll-up)Interactive analysis (drill-down, slice, dice, pivot,
roll-up) Fast response timeFast response time Minimizing storage
space usedMinimizing storage space used ChallengeChallenge A merged
region may contain hundreds of primitive regionsA merged region may
contain hundreds of primitive regions (polygons)(polygons)
Lecture-51 - Mining spatial databasesLecture-51 - Mining spatial
databases 16. Star Schema of the BCStar Schema of the BC Weather
WarehouseWeather Warehouse Spatial dataSpatial data
warehousewarehouse DimensionsDimensions region_nameregion_name
timetime temperaturetemperature precipitationprecipitation
MeasurementsMeasurements region_mapregion_map areaarea countcount
Fact tableDimension table Lecture-51 - Mining spatial
databasesLecture-51 - Mining spatial databases 17. Spatial
MergeSpatial Merge Precomputing all: too much storage space On-line
merge: very expensive Lecture-51 - Mining spatial
databasesLecture-51 - Mining spatial databases 18. Methods for
Computation of SpatialMethods for Computation of Spatial Data
CubeData Cube On-line aggregation: collect and store pointers to
spatialOn-line aggregation: collect and store pointers to spatial
objects in a spatial data cubeobjects in a spatial data cube
expensive and slow, need efficient aggregationexpensive and slow,
need efficient aggregation techniquestechniques Precompute and
store all the possible combinationsPrecompute and store all the
possible combinations huge space overheadhuge space overhead
Precompute and store rough approximations in a spatialPrecompute
and store rough approximations in a spatial data cubedata cube
accuracy trade-offaccuracy trade-off Selective computation: only
materialize those which will beSelective computation: only
materialize those which will be accessed frequentlyaccessed
frequently a reasonable choicea reasonable choice Lecture-51 -
Mining spatial databasesLecture-51 - Mining spatial databases 19.
Spatial Association AnalysisSpatial Association Analysis Spatial
association rule:Spatial association rule: AA BB [[ss%,%, cc%]%] A
and B are sets of spatial or nonspatial predicatesA and B are sets
of spatial or nonspatial predicates Topological
relations:Topological relations: intersects, overlaps,
disjoint,intersects, overlaps, disjoint, etc.etc. Spatial
orientations:Spatial orientations: left_of, west_of, under,left_of,
west_of, under, etc.etc. Distance information:Distance information:
close_to, within_distance,close_to, within_distance, etc.etc. ss%
is the support and% is the support and cc% is the confidence of the
rule% is the confidence of the rule ExamplesExamples is_a(x,
large_town) ^ intersect(x, highway)is_a(x, large_town) ^
intersect(x, highway) adjacent_to(x,adjacent_to(x, water) [7%,
85%]water) [7%, 85%] is_a(x, large_town) ^adjacent_to(x,
georgia_strait)is_a(x, large_town) ^adjacent_to(x, georgia_strait)
close_to(x, u.s.a.)close_to(x, u.s.a.) [1%, 78%][1%, 78%]
Lecture-51 - Mining spatial databasesLecture-51 - Mining spatial
databases 20. Progressive Refinement Mining of SpatialProgressive
Refinement Mining of Spatial Association RulesAssociation Rules
Hierarchy of spatial relationship:Hierarchy of spatial
relationship: g_close_tog_close_to:: near_bynear_by,, touchtouch,,
intersectintersect,, containcontain, etc., etc. First search for
rough relationship and then refine itFirst search for rough
relationship and then refine it Two-step mining of spatial
association:Two-step mining of spatial association: Step 1: Rough
spatial computation (as a filter)Step 1: Rough spatial computation
(as a filter) Using MBR or R-tree for rough estimationUsing MBR or
R-tree for rough estimation Step2: Detailed spatial algorithm (as
refinement)Step2: Detailed spatial algorithm (as refinement) Apply
only to those objects which have passed the roughApply only to
those objects which have passed the rough spatial association test
(no less thanspatial association test (no less than
min_supportmin_support)) Lecture-51 - Mining spatial
databasesLecture-51 - Mining spatial databases 21. Spatial
classificationSpatial classification Analyze spatial objects to
derive classificationAnalyze spatial objects to derive
classification schemes, such as decision trees in relevance
toschemes, such as decision trees in relevance to certain spatial
properties (district, highway, river, etc.)certain spatial
properties (district, highway, river, etc.) Example: Classify
regions in a province intoExample: Classify regions in a province
into richrich vs.vs. poorpoor according to the average family
incomeaccording to the average family income Spatial trend
analysisSpatial trend analysis Detect changes and trends along a
spatial dimensionDetect changes and trends along a spatial
dimension Study the trend of nonspatial or spatial data
changingStudy the trend of nonspatial or spatial data changing with
spacewith space Example: Observe the trend of changes of the
climateExample: Observe the trend of changes of the climate or
vegetation with the increasing distance from anor vegetation with
the increasing distance from an oceanocean Spatial Classification
and Spatial TrendSpatial Classification and Spatial Trend
AnalysisAnalysis Lecture-51 - Mining spatial databasesLecture-51 -
Mining spatial databases 22. Lecture-52Lecture-52 Mining multimedia
databasesMining multimedia databases 23. Similarity Search in
Multimedia DataSimilarity Search in Multimedia Data
Description-based retrieval systemsDescription-based retrieval
systems Build indices and perform object retrieval based onBuild
indices and perform object retrieval based on image descriptions,
such as keywords, captions,image descriptions, such as keywords,
captions, size, and time of creationsize, and time of creation
Labor-intensive if performed manuallyLabor-intensive if performed
manually Results are typically of poor quality if automatedResults
are typically of poor quality if automated Content-based retrieval
systemsContent-based retrieval systems Support retrieval based on
the image content, suchSupport retrieval based on the image
content, such as color histogram, texture, shape, objects, andas
color histogram, texture, shape, objects, and wavelet
transformswavelet transforms Lecture-52 - Mining multimedia
databasesLecture-52 - Mining multimedia databases 24. Queries in
Content-Based RetrievalQueries in Content-Based Retrieval
SystemsSystems Image sample-based queries:Image sample-based
queries: Find all of the images that are similar to the givenFind
all of the images that are similar to the given image sampleimage
sample Compare the feature vector (signature) extracted fromCompare
the feature vector (signature) extracted from the sample with the
feature vectors of images thatthe sample with the feature vectors
of images that have already been extracted and indexed in the
imagehave already been extracted and indexed in the image
databasedatabase Image feature specification queries:Image feature
specification queries: Specify or sketch image features like color,
texture, orSpecify or sketch image features like color, texture, or
shape, which are translated into a feature vectorshape, which are
translated into a feature vector Match the feature vector with the
feature vectors of theMatch the feature vector with the feature
vectors of the images in the databaseimages in the database
Lecture-52 - Mining multimedia databasesLecture-52 - Mining
multimedia databases 25. Approaches Based on ImageApproaches Based
on Image SignatureSignatureColor histogram-based signatureColor
histogram-based signature The signature includes color histograms
based on colorThe signature includes color histograms based on
color composition of an image regardless of its scale orcomposition
of an image regardless of its scale or orientationorientation No
information about shape, location, or textureNo information about
shape, location, or texture Two images with similar color
composition may containTwo images with similar color composition
may contain very different shapes or textures, and thus could
bevery different shapes or textures, and thus could be completely
unrelated in semanticscompletely unrelated in semantics
Multifeature composed signatureMultifeature composed signature The
signature includes a composition of multipleThe signature includes
a composition of multiple features: color histogram, shape,
location, and texturefeatures: color histogram, shape, location,
and texture Can be used to search for similar imagesCan be used to
search for similar images Lecture-52 - Mining multimedia
databasesLecture-52 - Mining multimedia databases 26. Wavelet
AnalysisWavelet Analysis Wavelet-based signatureWavelet-based
signature Use the dominant wavelet coefficients of an image as
itsUse the dominant wavelet coefficients of an image as its
signaturesignature Wavelets capture shape, texture, and
locationWavelets capture shape, texture, and location information
in a single unified frameworkinformation in a single unified
framework Improved efficiency and reduced the need for
providingImproved efficiency and reduced the need for providing
multiple search primitivesmultiple search primitives May fail to
identify images containing similar in locationMay fail to identify
images containing similar in location or size objectsor size
objects Wavelet-based signature with region-based
granularityWavelet-based signature with region-based granularity
Similar images may contain similar regions, but a regionSimilar
images may contain similar regions, but a region in one image could
be a translation or scaling of ain one image could be a translation
or scaling of a matching region in the othermatching region in the
other Compute and compare signatures at the granularity ofCompute
and compare signatures at the granularity of regions, not the
entire imageregions, not the entire image Lecture-52 - Mining
multimedia databasesLecture-52 - Mining multimedia databases 27.
C-BIRD:C-BIRD: CContent-ontent-BBasedased IImagemage RRetrieval
frometrieval from DDigital librariesigital libraries Search by
image colors by color percentage by color layout by texture density
by texture Layout by object model by illumination invariance by
keywords Lecture-52 - Mining multimedia databasesLecture-52 -
Mining multimedia databases 28. Multi-Dimensional Search
inMulti-Dimensional Search in Multimedia DatabasesMultimedia
DatabasesColor layout Lecture-52 - Mining multimedia
databasesLecture-52 - Mining multimedia databases 29. Color
histogram Texture layout Multi-Dimensional
AnalysisMulti-Dimensional Analysis in Multimedia Databasesin
Multimedia Databases Lecture-52 - Mining multimedia
databasesLecture-52 - Mining multimedia databases 30. Refining or
combining searches Search for blue sky (top layout grid is blue)
Search for blue sky and green meadows (top layout grid is blue and
bottom is green) Search for airplane in blue sky (top layout grid
is blue and keyword = airplane) Mining Multimedia DatabasesMining
Multimedia Databases Lecture-52 - Mining multimedia
databasesLecture-52 - Mining multimedia databases 31.
Multidimensional Analysis of MultimediaMultidimensional Analysis of
Multimedia DataData Multimedia data cubeMultimedia data cube Design
and construction similar to that of traditionalDesign and
construction similar to that of traditional data cubes from
relational datadata cubes from relational data Contain additional
dimensions and measures forContain additional dimensions and
measures for multimedia information, such as color, texture,
andmultimedia information, such as color, texture, and shapeshape
The database does not store images but theirThe database does not
store images but their descriptorsdescriptors Feature descriptor: a
set of vectors for each visualFeature descriptor: a set of vectors
for each visual characteristiccharacteristic Color vector: contains
the color histogramColor vector: contains the color histogram MFC
(Most Frequent Color) vector: five color centroidsMFC (Most
Frequent Color) vector: five color centroids MFO (Most Frequent
Orientation) vector: five edge orientationMFO (Most Frequent
Orientation) vector: five edge orientation centroidscentroids
Layout descriptor: contains a color layout vector and anLayout
descriptor: contains a color layout vector and an edge layout
vectoredge layout vectorLecture-52 - Mining multimedia
databasesLecture-52 - Mining multimedia databases 32. Mining
Multimedia Databases inMining Multimedia Databases in Lecture-52 -
Mining multimedia databasesLecture-52 - Mining multimedia databases
33. RED WHITE BLUE GIFJPEG By Format By Colour Sum Cross Tab RED
WHITE BLUE Colour Sum Group By Measurement JPEG GIF Small Very
Large RED WHITE BLUE By Colour By Format & Colour By Format
& Size By Colour & Size By Format By Size Sum The Data Cube
and the Sub-Space Measurements Medium Large Format of image
Duration Colors Textures Keywords Size Width Height Internet domain
of image Internet domain of parent pages Image popularity Mining
Multimedia DatabasesMining Multimedia Databases Lecture-52 - Mining
multimedia databasesLecture-52 - Mining multimedia databases 34.
Classification in MultiMediaMinerClassification in MultiMediaMiner
Lecture-52 - Mining multimedia databasesLecture-52 - Mining
multimedia databases 35. Special features:Special features: Need #
of occurrences besides Boolean existence, e.g.,Need # of
occurrences besides Boolean existence, e.g., Two red square and one
blue circle implies themeTwo red square and one blue circle implies
theme air-showair-show Need spatial relationshipsNeed spatial
relationships Blue on top of white squared object is associatedBlue
on top of white squared object is associated with brown bottomwith
brown bottom Need multi-resolution and progressive refinementNeed
multi-resolution and progressive refinement miningmining It is
expensive to explore detailed associationsIt is expensive to
explore detailed associations among objects at high resolutionamong
objects at high resolution It is crucial to ensure the completeness
of search atIt is crucial to ensure the completeness of search at
multi-resolution spacemulti-resolution space Mining Associations in
Multimedia Data Lecture-52 - Mining multimedia databasesLecture-52
- Mining multimedia databases 36. Spatial Relationships from Layout
property P1 next-to property P2property P1 on-top-of property P2
Different Resolution Hierarchy Mining Multimedia DatabasesMining
Multimedia Databases Lecture-52 - Mining multimedia
databasesLecture-52 - Mining multimedia databases 37. From Coarse
to Fine Resolution Mining Mining Multimedia DatabasesMining
Multimedia Databases Lecture-52 - Mining multimedia
databasesLecture-52 - Mining multimedia databases 38. Challenge:
Curse of DimensionalityChallenge: Curse of Dimensionality Difficult
to implement a data cube efficiently given aDifficult to implement
a data cube efficiently given a large number of dimensions,
especially serious in thelarge number of dimensions, especially
serious in the case of multimedia data cubescase of multimedia data
cubes Many of these attributes are set-oriented instead ofMany of
these attributes are set-oriented instead of
single-valuedsingle-valued Restricting number of dimensions may
lead to theRestricting number of dimensions may lead to the
modeling of an image at a rather rough, limited, andmodeling of an
image at a rather rough, limited, and imprecise scaleimprecise
scale More research is needed to strike a balance betweenMore
research is needed to strike a balance between efficiency and power
of representationefficiency and power of representation Lecture-52
- Mining multimedia databasesLecture-52 - Mining multimedia
databases 39. Lecture-53Lecture-53 Mining time-series and
sequenceMining time-series and sequence datadata 40. Mining
Time-Series and SequenceMining Time-Series and Sequence DataData
Time-series databaseTime-series database Consists of sequences of
values or eventsConsists of sequences of values or events changing
with timechanging with time Data is recorded at regular
intervalsData is recorded at regular intervals Characteristic
time-series componentsCharacteristic time-series components Trend,
cycle, seasonal, irregularTrend, cycle, seasonal, irregular
ApplicationsApplications Financial: stock price,
inflationFinancial: stock price, inflation Biomedical: blood
pressureBiomedical: blood pressure Meteorological:
precipitationMeteorological: precipitation Lecture-53 - Mining
time-series and sequence dataLecture-53 - Mining time-series and
sequence data 41. Mining Time-Series andMining Time-Series and
Sequence DataSequence Data Time-series plot Lecture-53 - Mining
time-series and sequence dataLecture-53 - Mining time-series and
sequence data 42. Mining Time-Series and Sequence Data:Mining
Time-Series and Sequence Data: Trend analysisTrend analysis A time
series can be illustrated as a time-series graphA time series can
be illustrated as a time-series graph which describes a point
moving with the passage of timewhich describes a point moving with
the passage of time Categories of Time-Series MovementsCategories
of Time-Series Movements Long-term or trend movements (trend
curve)Long-term or trend movements (trend curve) Cyclic movements
or cycle variations, e.g., businessCyclic movements or cycle
variations, e.g., business cyclescycles Seasonal movements or
seasonal variationsSeasonal movements or seasonal variations i.e,
almost identical patterns that a time seriesi.e, almost identical
patterns that a time series appears to follow during corresponding
months ofappears to follow during corresponding months of
successive years.successive years. Irregular or random
movementsIrregular or random movements Lecture-53 - Mining
time-series and sequence dataLecture-53 - Mining time-series and
sequence data 43. Estimation of Trend CurveEstimation of Trend
Curve The freehand methodThe freehand method Fit the curve by
looking at the graphFit the curve by looking at the graph Costly
and barely reliable for large-scaled data miningCostly and barely
reliable for large-scaled data mining The least-square methodThe
least-square method Find the curve minimizing the sum of the
squares ofFind the curve minimizing the sum of the squares of the
deviation of points on the curve from thethe deviation of points on
the curve from the corresponding data pointscorresponding data
points The moving-average methodThe moving-average method Eliminate
cyclic, seasonal and irregular patternsEliminate cyclic, seasonal
and irregular patterns Loss of end dataLoss of end data Sensitive
to outliersSensitive to outliers Lecture-53 - Mining time-series
and sequence dataLecture-53 - Mining time-series and sequence data
44. Discovery of Trend in Time-SeriesDiscovery of Trend in
Time-Series Estimation of seasonal variationsEstimation of seasonal
variations Seasonal indexSeasonal index Set of numbers showing the
relative values of a variable duringSet of numbers showing the
relative values of a variable during the months of the yearthe
months of the year E.g., if the sales during October, November, and
December areE.g., if the sales during October, November, and
December are 80%, 120%, and 140% of the average monthly sales for
the80%, 120%, and 140% of the average monthly sales for the whole
year, respectively, then 80, 120, and 140 are seasonalwhole year,
respectively, then 80, 120, and 140 are seasonal index numbers for
these monthsindex numbers for these months Deseasonalized
dataDeseasonalized data Data adjusted for seasonal variationsData
adjusted for seasonal variations E.g., divide the original monthly
data by the seasonal indexE.g., divide the original monthly data by
the seasonal index numbers for the corresponding monthsnumbers for
the corresponding months Lecture-53 - Mining time-series and
sequence dataLecture-53 - Mining time-series and sequence data 45.
Discovery of Trend in Time-SeriesDiscovery of Trend in Time-Series
Estimation of cyclic variationsEstimation of cyclic variations If
(approximate) periodicity of cycles occurs, cyclicIf (approximate)
periodicity of cycles occurs, cyclic index can be constructed in
much the same mannerindex can be constructed in much the same
manner as seasonal indexesas seasonal indexes Estimation of
irregular variationsEstimation of irregular variations By adjusting
the data for trend, seasonal and cyclicBy adjusting the data for
trend, seasonal and cyclic variationsvariations With the systematic
analysis of the trend, cyclic,With the systematic analysis of the
trend, cyclic, seasonal, and irregular components, it isseasonal,
and irregular components, it is possible to make long- or
short-term predictionspossible to make long- or short-term
predictions with reasonable qualitywith reasonable quality
Lecture-53 - Mining time-series and sequence dataLecture-53 -
Mining time-series and sequence data 46. Similarity Search in
Time-Series AnalysisSimilarity Search in Time-Series Analysis
Normal database query finds exact matchNormal database query finds
exact match Similarity search finds data sequences that
differSimilarity search finds data sequences that differ only
slightly from the given query sequenceonly slightly from the given
query sequence Two categories of similarity queriesTwo categories
of similarity queries Whole matching: find a sequence that is
similar to theWhole matching: find a sequence that is similar to
the query sequencequery sequence Subsequence matching: find all
pairs of similarSubsequence matching: find all pairs of similar
sequencessequences Typical ApplicationsTypical Applications
Financial marketFinancial market Market basket data analysisMarket
basket data analysis Scientific databasesScientific databases
Medical diagnosisMedical diagnosisLecture-53 - Mining time-series
and sequence dataLecture-53 - Mining time-series and sequence data
47. Multidimensional IndexingMultidimensional Indexing
Multidimensional indexMultidimensional index Constructed for
efficient accessing using theConstructed for efficient accessing
using the first few Fourier coefficientsfirst few Fourier
coefficients Use the index can to retrieve theUse the index can to
retrieve the sequences that are at most a certainsequences that are
at most a certain small distance away from the querysmall distance
away from the query sequencesequence Perform postprocessing by
computing thePerform postprocessing by computing the actual
distance between sequences inactual distance between sequences in
the time domain and discard any falsethe time domain and discard
any false matchesmatches Lecture-53 - Mining time-series and
sequence dataLecture-53 - Mining time-series and sequence data 48.
Subsequence MatchingSubsequence Matching Break each sequence into a
set of pieces ofBreak each sequence into a set of pieces of window
with lengthwindow with length ww Extract the features of the
subsequenceExtract the features of the subsequence inside the
windowinside the window Map each sequence to a trail in theMap each
sequence to a trail in the feature spacefeature space Divide the
trail of each sequence intoDivide the trail of each sequence into
subtrails and represent each of them withsubtrails and represent
each of them with minimum bounding rectangleminimum bounding
rectangle Use a multipiece assembly algorithm toUse a multipiece
assembly algorithm to search for longer sequence matchessearch for
longer sequence matches Lecture-53 - Mining time-series and
sequence dataLecture-53 - Mining time-series and sequence data 49.
Enhanced similarity search methodsEnhanced similarity search
methods Allow for gaps within a sequence or differences in
offsetsAllow for gaps within a sequence or differences in offsets
or amplitudesor amplitudes Normalize sequences with amplitude
scaling and offsetNormalize sequences with amplitude scaling and
offset translationtranslation Two subsequences are considered
similar if one liesTwo subsequences are considered similar if one
lies within an envelope ofwithin an envelope of width around the
other, ignoringwidth around the other, ignoring outliersoutliers
Two sequences are said to be similar if they have enoughTwo
sequences are said to be similar if they have enough
non-overlapping time-ordered pairs of similarnon-overlapping
time-ordered pairs of similar subsequencessubsequences Parameters
specified by a user or expert: sliding windowParameters specified
by a user or expert: sliding window size, width of an envelope for
similarity, maximum gap,size, width of an envelope for similarity,
maximum gap, and matching fractionand matching fraction Lecture-53
- Mining time-series and sequence dataLecture-53 - Mining
time-series and sequence data 50. Steps for performing a similarity
searchSteps for performing a similarity search Atomic
matchingAtomic matching Find all pairs of gap-free windows of a
small lengthFind all pairs of gap-free windows of a small length
that are similarthat are similar Window stitchingWindow stitching
Stitch similar windows to form pairs of large similarStitch similar
windows to form pairs of large similar subsequences allowing gaps
between atomicsubsequences allowing gaps between atomic
matchesmatches Subsequence OrderingSubsequence Ordering Linearly
order the subsequence matches toLinearly order the subsequence
matches to determine whether enough similar pieces existdetermine
whether enough similar pieces exist Lecture-53 - Mining time-series
and sequence dataLecture-53 - Mining time-series and sequence data
51. Query Languages for Time SequencesQuery Languages for Time
Sequences Time-sequence query languageTime-sequence query language
Should be able to specify sophisticated queries likeShould be able
to specify sophisticated queries like Find all of the sequences
that are similar to some sequence in classFind all of the sequences
that are similar to some sequence in class AA, but not similar to
any sequence in class, but not similar to any sequence in class BB
Should be able to support various kinds of queries: rangeShould be
able to support various kinds of queries: range queries, all-pair
queries, and nearest neighbor queriesqueries, all-pair queries, and
nearest neighbor queries Shape definition languageShape definition
language Allows users to define and query the overall shape of
timeAllows users to define and query the overall shape of time
sequencessequences Uses human readable series of sequence
transitions or macrosUses human readable series of sequence
transitions or macros Ignores the specific detailsIgnores the
specific details E.g., the pattern up, Up, UP can be used to
describeE.g., the pattern up, Up, UP can be used to describe
increasing degrees of rising slopesincreasing degrees of rising
slopes Macros: spike, valley, etc.Macros: spike, valley, etc.
Lecture-53 - Mining time-series and sequence dataLecture-53 -
Mining time-series and sequence data 52. Sequential Pattern
MiningSequential Pattern Mining Mining of frequently occurring
patterns related toMining of frequently occurring patterns related
to time or other sequencestime or other sequences Sequential
pattern mining usually concentrate onSequential pattern mining
usually concentrate on symbolic patternssymbolic patterns
ExamplesExamples Renting Star Wars, then Empire Strikes Back,
thenRenting Star Wars, then Empire Strikes Back, then Return of the
Jedi in that orderReturn of the Jedi in that order Collection of
ordered events within an intervalCollection of ordered events
within an interval ApplicationsApplications Targeted
marketingTargeted marketing Customer retentionCustomer retention
Weather predictionWeather prediction Lecture-53 - Mining
time-series and sequence dataLecture-53 - Mining time-series and
sequence data 53. Mining SequencesMining Sequences (cont.)(cont.)
CustId Video sequence 1 {(C), (H)} 2 {(AB), (C), (DFG)} 3 {(CEG)} 4
{(C), (DG), (H)} 5 {(H)} Customer-sequenceCustomer-sequence
Sequential patterns with supportSequential patterns with support
> 0.25> 0.25 {(C), (H)}{(C), (H)} {(C), (DG)}{(C), (DG)} Map
Large ItemsetsMap Large Itemsets Large Itemsets MappedID (C) 1 (D)
2 (G) 3 (DG) 4 (H) 5 Lecture-53 - Mining time-series and sequence
dataLecture-53 - Mining time-series and sequence data 54.
Sequential pattern mining: Cases andSequential pattern mining:
Cases and ParametersParameters Duration of a time sequenceDuration
of a time sequence TT Sequential pattern mining can then be
confined to theSequential pattern mining can then be confined to
the data within a specified durationdata within a specified
duration Ex. Subsequence corresponding to the year of 1999Ex.
Subsequence corresponding to the year of 1999 Ex. Partitioned
sequences, such as every year, orEx. Partitioned sequences, such as
every year, or every week after stock crashes, or every two
weeksevery week after stock crashes, or every two weeks before and
after a volcano eruptionbefore and after a volcano eruption Event
folding windowEvent folding window ww IfIf w = Tw = T,
time-insensitive frequent patterns are found, time-insensitive
frequent patterns are found IfIf w = 0w = 0 (no event sequence
folding), sequential(no event sequence folding), sequential
patterns are found where each event occurs at apatterns are found
where each event occurs at a distinct time instantdistinct time
instant IfIf 0 < w < T0 < w < T, sequences occurring
within the same, sequences occurring within the same periodperiod
ww are folded in the analysisare folded in the analysisLecture-53 -
Mining time-series and sequence dataLecture-53 - Mining time-series
and sequence data 55. Sequential pattern mining: Cases
andSequential pattern mining: Cases and ParametersParameters Time
interval,Time interval, intint, between events in the, between
events in the discovered patterndiscovered pattern intint = 0: no
interval gap is allowed, i.e., only strictly= 0: no interval gap is
allowed, i.e., only strictly consecutive sequences are
foundconsecutive sequences are found Ex. Find frequent patterns
occurring in consecutive weeksEx. Find frequent patterns occurring
in consecutive weeks min_intmin_int intint max_intmax_int: find
patterns that are: find patterns that are separated by at
leastseparated by at least min_intmin_int but at mostbut at most
max_intmax_int Ex. If a person rents movie A, it is likely she will
rent movie BEx. If a person rents movie A, it is likely she will
rent movie B within 30 days (within 30 days (intint 30)30) intint =
c= c 0: find patterns carrying an exact interval0: find patterns
carrying an exact interval Ex. Every time when Dow Jones drops more
than 5%, whatEx. Every time when Dow Jones drops more than 5%, what
will happen exactly two days later? (will happen exactly two days
later? (intint = 2)= 2) Lecture-53 - Mining time-series and
sequence dataLecture-53 - Mining time-series and sequence data 56.
Episodes and Sequential Pattern MiningEpisodes and Sequential
Pattern Mining MethodsMethods Other methods for specifying the
kinds ofOther methods for specifying the kinds of patternspatterns
Serial episodes: ASerial episodes: A BB Parallel episodes: A &
BParallel episodes: A & B Regular expressions: (A |
B)C*(DRegular expressions: (A | B)C*(D E)E) Methods for sequential
pattern miningMethods for sequential pattern mining Variations of
Apriori-like algorithms, e.g., GSPVariations of Apriori-like
algorithms, e.g., GSP Database projection-based pattern
growthDatabase projection-based pattern growth Similar to the
frequent pattern growth withoutSimilar to the frequent pattern
growth without candidate generationcandidate generation Lecture-53
- Mining time-series and sequence dataLecture-53 - Mining
time-series and sequence data 57. Periodicity AnalysisPeriodicity
Analysis Periodicity is everywhere: tides, seasons, daily
powerPeriodicity is everywhere: tides, seasons, daily power
consumption, etc.consumption, etc. Full periodicityFull periodicity
Every point in time contributes (precisely orEvery point in time
contributes (precisely or approximately) to the
periodicityapproximately) to the periodicity Partial periodicit: A
more general notionPartial periodicit: A more general notion Only
some segments contribute to the periodicityOnly some segments
contribute to the periodicity Jim reads NY Times 7:00-7:30 am every
week dayJim reads NY Times 7:00-7:30 am every week day Cyclic
association rulesCyclic association rules Associations which form
cyclesAssociations which form cycles MethodsMethods Full
periodicity: FFT, other statistical analysis methodsFull
periodicity: FFT, other statistical analysis methods Partial and
cyclic periodicity: Variations of Apriori-likePartial and cyclic
periodicity: Variations of Apriori-like mining methodsmining
methods Lecture-53 - Mining time-series and sequence dataLecture-53
- Mining time-series and sequence data 58. Lecture-54Lecture-54
Mining text databasesMining text databases 59. Text Databases and
IRText Databases and IR Text databases (document databases)Text
databases (document databases) Large collections of documents from
various sources:Large collections of documents from various
sources: news articles, research papers, books, digital
libraries,news articles, research papers, books, digital libraries,
e-mail messages, and Web pages, library database,e-mail messages,
and Web pages, library database, etc.etc. Data stored is
usuallyData stored is usually semi-structuredsemi-structured
Traditional information retrieval techniques becomeTraditional
information retrieval techniques become inadequate for the
increasingly vast amounts of textinadequate for the increasingly
vast amounts of text datadata Information retrievalInformation
retrieval A field developed in parallel with database systemsA
field developed in parallel with database systems Information is
organized into (a large number of)Information is organized into (a
large number of) documentsdocuments Information retrieval problem:
locating relevantInformation retrieval problem: locating relevant
documents based on user input, such as keywords ordocuments based
on user input, such as keywords or example documentsexample
documents Lecture-54 - Mining text databasesLecture-54 - Mining
text databases 60. Information RetrievalInformation Retrieval
Typical IR systemsTypical IR systems Online library catalogsOnline
library catalogs Online document management systemsOnline document
management systems Information retrieval vs. database
systemsInformation retrieval vs. database systems Some DB problems
are not present in IR, e.g., update,Some DB problems are not
present in IR, e.g., update, transaction management, complex
objectstransaction management, complex objects Some IR problems are
not addressed well in DBMS,Some IR problems are not addressed well
in DBMS, e.g., unstructured documents, approximate searche.g.,
unstructured documents, approximate search using keywords and
relevanceusing keywords and relevance Lecture-54 - Mining text
databasesLecture-54 - Mining text databases 61. Basic Measures for
TextBasic Measures for Text RetrievalRetrieval Precision:Precision:
the percentage of retrieved documentsthe percentage of retrieved
documents that are in fact relevant to the query (i.e., correctthat
are in fact relevant to the query (i.e., correct
responses)responses) Recall:Recall: the percentage of documents
that arethe percentage of documents that are relevant to the query
and were, in fact, retrievedrelevant to the query and were, in
fact, retrieved|}{| |}{}{| Relevant RetrievedRelevant precision =
|}{| |}{}{| Retrieved RetrievedRelevant precision = Lecture-54 -
Mining text databasesLecture-54 - Mining text databases 62.
Keyword-Based RetrievalKeyword-Based Retrieval A document is
represented by a string, whichA document is represented by a
string, which can be identified by a set of keywordscan be
identified by a set of keywords Queries may use expressions of
keywordsQueries may use expressions of keywords E.g., carE.g., car
andand repair shop, tearepair shop, tea oror coffee, DBMScoffee,
DBMS butbut notnot OracleOracle Queries and retrieval should
consider synonyms,Queries and retrieval should consider synonyms,
e.g., repair and maintenancee.g., repair and maintenance Major
difficulties of the modelMajor difficulties of the model Synonymy:
A keywordSynonymy: A keyword TT does not appear anywheredoes not
appear anywhere in the document, even though the document isin the
document, even though the document is closely related toclosely
related to TT, e.g., data mining, e.g., data mining Polysemy: The
same keyword may mean differentPolysemy: The same keyword may mean
different things in different contexts, e.g., miningthings in
different contexts, e.g., mining Lecture-54 - Mining text
databasesLecture-54 - Mining text databases 63. Similarity-Based
Retrieval in TextSimilarity-Based Retrieval in Text
DatabasesDatabases Finds similar documents based on a set of
commonFinds similar documents based on a set of common
keywordskeywords Answer should be based on the degree of
relevanceAnswer should be based on the degree of relevance based on
the nearness of the keywords, relativebased on the nearness of the
keywords, relative frequency of the keywords, etc.frequency of the
keywords, etc. Basic techniquesBasic techniques Stop listStop list
Set of words that are deemed irrelevant, evenSet of words that are
deemed irrelevant, even though they may appear frequentlythough
they may appear frequently E.g.,E.g., a, the, of, for, witha, the,
of, for, with, etc., etc. Stop lists may vary when document set
variesStop lists may vary when document set varies Lecture-54 -
Mining text databasesLecture-54 - Mining text databases 64.
Similarity-Based Retrieval in TextSimilarity-Based Retrieval in
Text DatabasesDatabases Word stemWord stem Several words are small
syntactic variants of eachSeveral words are small syntactic
variants of each other since they share a common word stemother
since they share a common word stem E.g.,E.g., drugdrug,, drugs,
druggeddrugs, drugged A term frequency tableA term frequency table
Each entryEach entry frequent_table(i, j)frequent_table(i, j) = #
of occurrences of= # of occurrences of the wordthe word ttii in
documentin document ddii Usually, theUsually, the ratioratio
instead of the absolute number ofinstead of the absolute number of
occurrences is usedoccurrences is used Similarity metrics: measure
the closeness of aSimilarity metrics: measure the closeness of a
document to a query (a set of keywords)document to a query (a set
of keywords) Relative term occurrencesRelative term occurrences
Cosine distance:Cosine distance: |||| ),( 21 21 21 vv vv vvsim =
Lecture-54 - Mining text databasesLecture-54 - Mining text
databases 65. Latent Semantic IndexingLatent Semantic Indexing
Basic ideaBasic idea Similar documents have similar word
frequenciesSimilar documents have similar word frequencies
Difficulty: the size of the term frequency matrix is very
largeDifficulty: the size of the term frequency matrix is very
large Use a singular value decomposition (SVD) techniques to
reduceUse a singular value decomposition (SVD) techniques to reduce
the size of frequency tablethe size of frequency table Retain
theRetain the KK most significant rows of the frequency tablemost
significant rows of the frequency table MethodMethod Create a term
frequency matrix,Create a term frequency matrix,
freq_matrixfreq_matrix SVD construction: Compute the singular
valued decomposition ofSVD construction: Compute the singular
valued decomposition of freq_matrixfreq_matrix by splitting it into
3 matrices,by splitting it into 3 matrices, UU,, SS,, VV Vector
identification: For each documentVector identification: For each
document dd, replace its original, replace its original document
vector by a new excluding the eliminated termsdocument vector by a
new excluding the eliminated terms Index creation: Store the set of
all vectors, indexed by one of aIndex creation: Store the set of
all vectors, indexed by one of a number of techniques (such as
TV-tree)number of techniques (such as TV-tree) Lecture-54 - Mining
text databasesLecture-54 - Mining text databases 66. Other Text
Retrieval Indexing TechniquesOther Text Retrieval Indexing
Techniques Inverted indexInverted index Maintains two hash- or
B+-tree indexed tables:Maintains two hash- or B+-tree indexed
tables: document_table: a set of document records postings_list>
term_table: a set of term records, term_table: a set of term
records, Answer query: Find all docs associated with one or a set
of termsAnswer query: Find all docs associated with one or a set of
terms Advantage: easy to implementAdvantage: easy to implement
Disadvantage: do not handle well synonymy and polysemy,
andDisadvantage: do not handle well synonymy and polysemy, and
posting lists could be too long (storage could be very
large)posting lists could be too long (storage could be very large)
Signature fileSignature file Associate a signature with each
documentAssociate a signature with each document A signature is a
representation of an ordered list of terms thatA signature is a
representation of an ordered list of terms that describe the
documentdescribe the document Order is obtained by frequency
analysis, stemming and stop listsOrder is obtained by frequency
analysis, stemming and stop lists Lecture-54 - Mining text
databasesLecture-54 - Mining text databases 67. Types of Text Data
MiningTypes of Text Data Mining Keyword-based association
analysisKeyword-based association analysis Automatic document
classificationAutomatic document classification Similarity
detectionSimilarity detection Cluster documents by a common
authorCluster documents by a common author Cluster documents
containing information from aCluster documents containing
information from a common sourcecommon source Link analysis:
unusual correlation between entitiesLink analysis: unusual
correlation between entities Sequence analysis: predicting a
recurring eventSequence analysis: predicting a recurring event
Anomaly detection: find information that violates usualAnomaly
detection: find information that violates usual patternspatterns
Hypertext analysisHypertext analysis Patterns in
anchors/linksPatterns in anchors/links Anchor text correlations
with linked objectsAnchor text correlations with linked objects
Lecture-54 - Mining text databasesLecture-54 - Mining text
databases 68. Keyword-based association analysisKeyword-based
association analysis Collect sets of keywords or terms that
occurCollect sets of keywords or terms that occur frequently
together and then find the associationfrequently together and then
find the association or correlation relationships among themor
correlation relationships among them First preprocess the text data
by parsing,First preprocess the text data by parsing, stemming,
removing stop words, etc.stemming, removing stop words, etc. Then
evoke association mining algorithmsThen evoke association mining
algorithms Consider each document as a transactionConsider each
document as a transaction View a set of keywords in the document as
a set ofView a set of keywords in the document as a set of items in
the transactionitems in the transaction Term level association
miningTerm level association mining No need for human effort in
tagging documentsNo need for human effort in tagging documents The
number of meaningless results and the executionThe number of
meaningless results and the execution time is greatly reducedtime
is greatly reduced Lecture-54 - Mining text databasesLecture-54 -
Mining text databases 69. Automatic document
classificationAutomatic document classification
MotivationMotivation Automatic classification for the tremendous
number ofAutomatic classification for the tremendous number of
on-line text documents (Web pages, e-mails, etc.)on-line text
documents (Web pages, e-mails, etc.) A classification problemA
classification problem Training set: Human experts generate a
training data setTraining set: Human experts generate a training
data set Classification: The computer system discovers
theClassification: The computer system discovers the classification
rulesclassification rules Application: The discovered rules can be
applied toApplication: The discovered rules can be applied to
classify new/unknown documentsclassify new/unknown documents Text
document classification differs from theText document
classification differs from the classification of relational
dataclassification of relational data Document databases are not
structured according toDocument databases are not structured
according to attribute-value pairsattribute-value pairs Lecture-54
- Mining text databasesLecture-54 - Mining text databases 70.
Association-Based DocumentAssociation-Based Document
ClassificationClassification Extract keywords and terms by
information retrieval and simpleExtract keywords and terms by
information retrieval and simple association analysis
techniquesassociation analysis techniques Obtain concept
hierarchies of keywords and terms usingObtain concept hierarchies
of keywords and terms using Available term classes, such as
WordNetAvailable term classes, such as WordNet Expert
knowledgeExpert knowledge Some keyword classification systemsSome
keyword classification systems Classify documents in the training
set into class hierarchiesClassify documents in the training set
into class hierarchies Apply term association mining method to
discover sets of associatedApply term association mining method to
discover sets of associated termsterms Use the terms to maximally
distinguish one class of documents fromUse the terms to maximally
distinguish one class of documents from othersothers Derive a set
of association rules associated with each documentDerive a set of
association rules associated with each document classclass Order
the classification rules based on their occurrence frequencyOrder
the classification rules based on their occurrence frequency and
discriminative powerand discriminative power Used the rules to
classify new documentsUsed the rules to classify new documents
Lecture-54 - Mining text databasesLecture-54 - Mining text
databases 71. Document ClusteringDocument Clustering Automatically
group related documents based on theirAutomatically group related
documents based on their contentscontents Require no training sets
or predetermined taxonomies,Require no training sets or
predetermined taxonomies, generate a taxonomy at runtimegenerate a
taxonomy at runtime Major stepsMajor steps
PreprocessingPreprocessing Remove stop words, stem, feature
extraction, lexicalRemove stop words, stem, feature extraction,
lexical analysis, analysis, Hierarchical clusteringHierarchical
clustering Compute similarities applying clustering
algorithms,Compute similarities applying clustering algorithms,
SlicingSlicing Fan out controls, flatten the tree to
configurableFan out controls, flatten the tree to configurable
number of levels, number of levels, Lecture-54 - Mining text
databasesLecture-54 - Mining text databases 72.
Lecture-55Lecture-55 Mining the World-Wide WebMining the World-Wide
Web 73. Mining the World-Wide WebMining the World-Wide Web The WWW
is huge, widely distributed, globalThe WWW is huge, widely
distributed, global information service center forinformation
service center for Information services: news,
advertisements,Information services: news, advertisements, consumer
information, financial management,consumer information, financial
management, education, government, e-commerce, etc.education,
government, e-commerce, etc. Hyper-link informationHyper-link
information Access and usage informationAccess and usage
information WWW provides rich sources for data miningWWW provides
rich sources for data mining ChallengesChallenges Too huge for
effective data warehousing and dataToo huge for effective data
warehousing and data miningmining Too complex and heterogeneous: no
standards andToo complex and heterogeneous: no standards and
structurestructure Lecture-55 - Mining the World-Wide WebLecture-55
- Mining the World-Wide Web 74. Mining the World-Wide WebMining the
World-Wide Web Growing and changing very rapidlyGrowing and
changing very rapidly Broad diversity of user communitiesBroad
diversity of user communities Only a small portion of the
information on the Web is trulyOnly a small portion of the
information on the Web is truly relevant or usefulrelevant or
useful 99% of the Web information is useless to 99% of Web users99%
of the Web information is useless to 99% of Web users How can we
find high-quality Web pages on a specified topic?How can we find
high-quality Web pages on a specified topic? Internet growth 0
5000000 10000000 15000000 20000000 25000000 30000000 35000000
40000000 Sep-69 Sep-72 Sep-75 Sep-78 Sep-81 Sep-84 Sep-87 Sep-90
Sep-93 Sep-96 Sep-99 Hosts Lecture-55 - Mining the World-Wide
WebLecture-55 - Mining the World-Wide Web 75. Web search enginesWeb
search engines Index-based: search the Web, index Web
pages,Index-based: search the Web, index Web pages, and build and
store huge keyword-based indicesand build and store huge
keyword-based indices Help locate sets of Web pages containing
certainHelp locate sets of Web pages containing certain
keywordskeywords DeficienciesDeficiencies A topic of any breadth
may easily containA topic of any breadth may easily contain
hundreds of thousands of documentshundreds of thousands of
documents Many documents that are highly relevant to aMany
documents that are highly relevant to a topic may not contain
keywords defining themtopic may not contain keywords defining them
(polysemy)(polysemy) Lecture-55 - Mining the World-Wide
WebLecture-55 - Mining the World-Wide Web 76. Web Mining: A more
challenging taskWeb Mining: A more challenging task Searches
forSearches for Web access patternsWeb access patterns Web
structuresWeb structures Regularity and dynamics of Web
contentsRegularity and dynamics of Web contents ProblemsProblems
The abundance problemThe abundance problem Limited coverage of the
Web: hidden Web sources,Limited coverage of the Web: hidden Web
sources, majority of data in DBMSmajority of data in DBMS Limited
query interface based on keyword-orientedLimited query interface
based on keyword-oriented searchsearch Limited customization to
individual usersLimited customization to individual users
Lecture-55 - Mining the World-Wide WebLecture-55 - Mining the
World-Wide Web 77. Web Mining Web Structure Mining Web Content
Mining Web Page Content Mining Search Result Mining Web Usage
Mining General Access Pattern Tracking Customized Usage Tracking
Web Mining TaxonomyWeb Mining Taxonomy Lecture-55 - Mining the
World-Wide WebLecture-55 - Mining the World-Wide Web 78. Web Mining
Web Structure Mining Web Content Mining Web Page Content Mining Web
Page Summarization WebLog (Lakshmanan et.al. 1996),
WebOQL(Mendelzon et.al. 1998) : Web Structuring query languages;
Can identify information within given web pages Ahoy! (Etzioni
et.al. 1997):Uses heuristics to distinguish personal home pages
from other web pages ShopBot (Etzioni et.al. 1997): Looks for
product prices within web pages Search Result Mining Web Usage
Mining General Access Pattern Tracking Customized Usage Tracking
Mining the World-Wide WebMining the World-Wide Web Lecture-55 -
Mining the World-Wide WebLecture-55 - Mining the World-Wide Web 79.
Web Mining Mining the World-Wide WebMining the World-Wide Web Web
Usage Mining General Access Pattern Tracking Customized Usage
Tracking Web Structure Mining Web Content Mining Web Page Content
Mining Search Result Mining Search Engine Result Summarization
Clustering Search Result (Leouski and Croft, 1996, Zamir and
Etzioni, 1997): Categorizes documents using phrases in titles and
snippets Lecture-55 - Mining the World-Wide WebLecture-55 - Mining
the World-Wide Web 80. Web Mining Web Content Mining Web Page
Content Mining Search Result Mining Web Usage Mining General Access
Pattern Tracking Customized Usage Tracking Mining the World-Wide
WebMining the World-Wide Web Web Structure Mining Using Links
PageRank (Brin et al., 1998) CLEVER (Chakrabarti et al., 1998) Use
interconnections between web pages to give weight to pages. Using
Generalization MLDB (1994), VWV (1998) Uses a multi-level database
representation of the Web. Counters (popularity) and link lists are
used for capturing structure. Lecture-55 - Mining the World-Wide
WebLecture-55 - Mining the World-Wide Web 81. Web Mining Web
Structure Mining Web Content Mining Web Page Content Mining Search
Result Mining Web Usage Mining General Access Pattern Tracking Web
Log Mining (Zaane, Xin and Han, 1998) Uses KDD techniques to
understand general access patterns and trends. Can shed light on
better structure and grouping of resource providers. Customized
Usage Tracking Mining the World-Wide WebMining the World-Wide Web
Lecture-55 - Mining the World-Wide WebLecture-55 - Mining the
World-Wide Web 82. Web Mining Web Usage Mining General Access
Pattern Tracking Customized Usage Tracking Adaptive Sites
(Perkowitz and Etzioni, 1997) Analyzes access patterns of each user
at a time. Web site restructures itself automatically by learning
from user access patterns. Mining the World-Wide WebMining the
World-Wide Web Web Structure Mining Web Content Mining Web Page
Content Mining Search Result Mining Lecture-55 - Mining the
World-Wide WebLecture-55 - Mining the World-Wide Web 83. Mining the
Web's Link StructuresMining the Web's Link Structures Finding
authoritative Web pagesFinding authoritative Web pages Retrieving
pages that are not only relevant, but also ofRetrieving pages that
are not only relevant, but also of high quality, or authoritative
on the topichigh quality, or authoritative on the topic Hyperlinks
can infer the notion of authorityHyperlinks can infer the notion of
authority The Web consists not only of pages, but also ofThe Web
consists not only of pages, but also of hyperlinks pointing from
one page to anotherhyperlinks pointing from one page to another
These hyperlinks contain an enormous amount ofThese hyperlinks
contain an enormous amount of latent human annotationlatent human
annotation A hyperlink pointing to another Web page, this can beA
hyperlink pointing to another Web page, this can be considered as
the author's endorsement of the otherconsidered as the author's
endorsement of the other pagepage Lecture-55 - Mining the
World-Wide WebLecture-55 - Mining the World-Wide Web 84. Mining the
Web's Link StructuresMining the Web's Link Structures Problems with
the Web linkage structureProblems with the Web linkage structure
Not every hyperlink represents an endorsementNot every hyperlink
represents an endorsement Other purposes are for navigation or for
paidOther purposes are for navigation or for paid
advertisementsadvertisements If the majority of hyperlinks are for
endorsement,If the majority of hyperlinks are for endorsement, the
collective opinion will still dominatethe collective opinion will
still dominate One authority will seldom have its Web page point
toOne authority will seldom have its Web page point to its rival
authorities in the same fieldits rival authorities in the same
field Authoritative pages are seldom particularlyAuthoritative
pages are seldom particularly descriptivedescriptive HubHub Set of
Web pages that provides collections of links toSet of Web pages
that provides collections of links to authoritiesauthorities
Lecture-55 - Mining the World-Wide WebLecture-55 - Mining the
World-Wide Web 85. HITS (HITS (HHyperlink-yperlink-IInducednduced
TTopicopic SSearch)earch) Explore interactions between hubs and
authoritativeExplore interactions between hubs and authoritative
pagespages Use an index-based search engine to form the root setUse
an index-based search engine to form the root set Many of these
pages are presumably relevant to theMany of these pages are
presumably relevant to the search topicsearch topic Some of them
should contain links to most of theSome of them should contain
links to most of the prominent authoritiesprominent authorities
Expand the root set into a base setExpand the root set into a base
set Include all of the pages that the root-set pages link
to,Include all of the pages that the root-set pages link to, and
all of the pages that link to a page in the root set,and all of the
pages that link to a page in the root set, up to a designated size
cutoffup to a designated size cutoff Apply weight-propagationApply
weight-propagation An iterative process that determines numericalAn
iterative process that determines numerical estimates of hub and
authority weightsestimates of hub and authority weights Lecture-55
- Mining the World-Wide WebLecture-55 - Mining the World-Wide Web
86. Systems Based on HITSSystems Based on HITS Output a short list
of the pages with large hub weights,Output a short list of the
pages with large hub weights, and the pages with large authority
weights for the givenand the pages with large authority weights for
the given search topicsearch topic Systems based on the HITS
algorithmSystems based on the HITS algorithm Clever, Google:
achieve better quality search resultsClever, Google: achieve better
quality search results than those generated by term-index engines
such asthan those generated by term-index engines such as AltaVista
and those created by human ontologists suchAltaVista and those
created by human ontologists such as Yahoo!as Yahoo! Difficulties
from ignoring textual contextsDifficulties from ignoring textual
contexts Drifting: when hubs contain multiple topicsDrifting: when
hubs contain multiple topics Topic hijacking: when many pages from
a single WebTopic hijacking: when many pages from a single Web site
point to the same single popular sitesite point to the same single
popular site Lecture-55 - Mining the World-Wide WebLecture-55 -
Mining the World-Wide Web 87. Automatic Classification of
WebAutomatic Classification of Web DocumentsDocuments Assign a
class label to each document from a set ofAssign a class label to
each document from a set of predefined topic categoriespredefined
topic categories Based on a set of examples of preclassified
documentsBased on a set of examples of preclassified documents
ExampleExample Use Yahoo!'s taxonomy and its associatedUse Yahoo!'s
taxonomy and its associated documents as training and test
setsdocuments as training and test sets Derive a Web document
classification schemeDerive a Web document classification scheme
Use the scheme classify new Web documents byUse the scheme classify
new Web documents by assigning categories from the same
taxonomyassigning categories from the same taxonomy Keyword-based
document classification methodsKeyword-based document
classification methods Statistical modelsStatistical models
Lecture-55 - Mining the World-Wide WebLecture-55 - Mining the
World-Wide Web 88. Multilayered Web Information BaseMultilayered
Web Information Base LayerLayer00:: the Web itselfthe Web itself
LayerLayer11:: the Web page descriptor layerthe Web page descriptor
layer Contains descriptive information for pages on the WebContains
descriptive information for pages on the Web An abstraction of
LayerAn abstraction of Layer00: substantially smaller but still:
substantially smaller but still rich enough to preserve most of the
interesting, generalrich enough to preserve most of the
interesting, general informationinformation Organized into dozens
of semistructured classesOrganized into dozens of semistructured
classes document, person, organization, ads, directory,document,
person, organization, ads, directory, sales, software, game,
stocks, library_catalog,sales, software, game, stocks,
library_catalog, geographic_data, scientific_datageographic_data,
scientific_data, etc., etc. LayerLayer22 and up:and up: various Web
directory services constructedvarious Web directory services
constructed on top ofon top of LayerLayer11 provide
multidimensional, application-specific servicesprovide
multidimensional, application-specific services Lecture-55 - Mining
the World-Wide WebLecture-55 - Mining the World-Wide Web 89.
Multiple Layered Web ArchitectureMultiple Layered Web Architecture
Generalized Descriptions More Generalized Descriptions Layer0
Layer1 Layern ... Lecture-55 - Mining the World-Wide WebLecture-55
- Mining the World-Wide Web 90. Mining the World-Wide WebMining the
World-Wide Web Layer-0: Primitive data Layer-1: dozen database
relations representing types of objects (metadata) document,
organization, person, software, game, map, image,
document(file_addr, authors, title, publication, publication_date,
abstract, language, table_of_contents, category_description,
keywords, index, multimedia_attached, num_pages, format,
first_paragraphs, size_doc, timestamp, access_frequency,
links_out,...) person(last_name, first_name, home_page_addr,
position, picture_attached, phone, e-mail, office_address,
education, research_interests, publications, size_of_home_page,
timestamp, access_frequency, ...) image(image_addr, author, title,
publication_date, category_description, keywords, size, width,
height, duration, format, parent_pages, colour_histogram,
Colour_layout, Texture_layout, Movement_vector,
localisation_vector, timestamp, access_frequency, ...) Lecture-55 -
Mining the World-Wide WebLecture-55 - Mining the World-Wide Web 91.
Mining the World-Wide WebMining the World-Wide Web
doc_brief(file_addr, authors, title, publication, publication_date,
abstract, language, category_description, key_words, major_index,
num_pages, format, size_doc, access_frequency, links_out)
person_brief (last_name, first_name, publications,affiliation,
e-mail, research_interests, size_home_page, access_frequency)
Layer-2: simplification of layer-1 Layer-3: generalization of
layer-2 cs_doc(file_addr, authors, title, publication,
publication_date, abstract, language, category_description,
keywords, num_pages, form, size_doc, links_out)
doc_summary(affiliation, field, publication_year, count,
first_author_list, file_addr_list) doc_author_brief(file_addr,
authors, affiliation, title, publication, pub_date,
category_description, keywords, num_pages, format, size_doc,
links_out) person_summary(affiliation, research_interest, year,
num_publications, count) Lecture-55 - Mining the World-Wide
WebLecture-55 - Mining the World-Wide Web 92. XML and Web MiningXML
and Web Mining XML can help to extract the correct descriptorsXML
can help to extract the correct descriptors Standardization would
greatly facilitate informationStandardization would greatly
facilitate information extractionextraction Potential
problemPotential problem XML can help solve heterogeneity for
vertical applications, butXML can help solve heterogeneity for
vertical applications, but the freedom to define tags can make
horizontal applicationsthe freedom to define tags can make
horizontal applications on the Web more heterogeneouson the Web
more heterogeneous eXtensible Markup LanguageWorld-Wide Web
Consortium19981.0Meta language that facilitates more meaningful and
precise declarations of document contentDefinition of new tags and
DTDs Lecture-55 - Mining the World-Wide WebLecture-55 - Mining the
World-Wide Web 93. Benefits of Multi-Layer Meta-WebBenefits of
Multi-Layer Meta-Web Benefits:Benefits: Multi-dimensional Web info
summary analysisMulti-dimensional Web info summary analysis
Approximate and intelligent query answeringApproximate and
intelligent query answering Web high-level query answering (WebSQL,
WebML)Web high-level query answering (WebSQL, WebML) Web content
and structure miningWeb content and structure mining Observing the
dynamics/evolution of the WebObserving the dynamics/evolution of
the Web Is it realistic to construct such a meta-Web?Is it
realistic to construct such a meta-Web? Benefits even if it is
partially constructedBenefits even if it is partially constructed
Benefits may justify the cost of tool development,Benefits may
justify the cost of tool development, standardization and partial
restructuringstandardization and partial restructuring Lecture-55 -
Mining the World-Wide WebLecture-55 - Mining the World-Wide Web 94.
Web Usage MiningWeb Usage Mining Mining Web log records to discover
user access patternsMining Web log records to discover user access
patterns of Web pagesof Web pages ApplicationsApplications Target
potential customers for electronic commerceTarget potential
customers for electronic commerce Enhance the quality and delivery
of Internet informationEnhance the quality and delivery of Internet
information services to the end userservices to the end user
Improve Web server system performanceImprove Web server system
performance Identify potential prime advertisement
locationsIdentify potential prime advertisement locations Web logs
provide rich information about Web dynamicsWeb logs provide rich
information about Web dynamics Typical Web log entry includes the
URL requested, theTypical Web log entry includes the URL requested,
the IP address from which the request originated, and aIP address
from which the request originated, and a timestamptimestamp
Lecture-55 - Mining the World-Wide WebLecture-55 - Mining the
World-Wide Web 95. Techniques for Web usage miningTechniques for
Web usage mining Construct multidimensional view on the Weblog
databaseConstruct multidimensional view on the Weblog database
Perform multidimensional OLAP analysis to find the topPerform
multidimensional OLAP analysis to find the top NN users, topusers,
top NN accessed Web pages, most frequentlyaccessed Web pages, most
frequently accessed time periods, etc.accessed time periods, etc.
Perform data mining on Weblog recordsPerform data mining on Weblog
records Find association patterns, sequential patterns, andFind
association patterns, sequential patterns, and trends of Web
accessingtrends of Web accessing May need additional
information,e.g., user browsingMay need additional
information,e.g., user browsing sequences of the Web pages in the
Web server buffersequences of the Web pages in the Web server
buffer Conduct studies toConduct studies to Analyze system
performance, improve system designAnalyze system performance,
improve system design by Web caching, Web page prefetching, and Web
pageby Web caching, Web page prefetching, and Web page
swappingswapping Lecture-55 - Mining the World-Wide WebLecture-55 -
Mining the World-Wide Web 96. Mining the World-Wide WebMining the
World-Wide Web Design of a Web Log MinerDesign of a Web Log Miner
Web log is filtered to generate a relational databaseWeb log is
filtered to generate a relational database A data cube is generated
form databaseA data cube is generated form database OLAP is used to
drill-down and roll-up in the cubeOLAP is used to drill-down and
roll-up in the cube OLAM is used for mining interesting
knowledgeOLAM is used for mining interesting knowledge 1 Data
Cleaning 2 Data Cube Creation 3 OLAP 4 Data Mining Web log Database
Data Cube Sliced and diced cube Knowledge Lecture-55 - Mining the
World-Wide WebLecture-55 - Mining the World-Wide Web