Data Mining - Functionalities,Classification and Task Primitives

5/12/2018 Data Mining - Functionalities,Classification and Task Primitives - slidepdf.com

http://slidepdf.com/reader/full/data-mining-functionalitiesclassification-and-task-primitives 1/24

PRESENT BY

K.Aravind (10mx03)

M.Boobalan (10mx05)

V.Boopathiraj (10mx06)

S.Kadhiresan(10mx18)

L.RoshanAli (10mx41)

A.Selvaraj (10mx46)

Data Mining: Functionalities,

Classification and Task Primitives



Data Mining Functionalities

It includes

Characterization and Discrimination

Mining Frequent Patterns, Associations, and Correlations

C

lassification and Regression Clustering Analysis

Outlier Analysis




y Class/Concept Description: Characterization and

Discrimination

y Data entries can be associated with classes or concepts

y

describe individual classes and concepts in summarized, concise,and precise terms. Such descriptions of a class or a concept are

called class/concept descriptions.




y Data characterization is a summarization of the general

characteristics or features of a target class of data.

y Data discrimination is a comparison of the general

features of the target class data objects against the generalfeatures of objects from one or multiple contrasting classes.



Mining Frequent Patterns,

Association &Correlation

y Frequent Patterns are the patterns that occur simultaneously.

y frequent patterns, including frequent itemsets, frequent

subsequences (sequential patterns), and frequent substructures

yAssociation Rulesy Single dimensional association rules vs Multi dimensional

association rules



Association rules are discarded as uninteresting if they do not

satisfy both a minimum support threshold and a

minimum confidence threshold.



Classification and Regression

y Classification is the process of finding a model (or function)

that describes and distinguishes data classes or concepts for

future prediction

y C

lass label is knowny E.g., classify countries based on climate, or classify cars based on

gas mileage

y Presentation: decision-tree, classification rule, neural network

y Prediction: Predict some unknown or missing numerical values



Cluster Analysis

y Class label is unknown: Group data to form new classes, e.g.,

cluster houses to find distribution patterns

y Clustering based on the principle:Maximizing the Intra-

class similarity andMinimizing the Interclasssimilarity



Outlier Analysis

y Outlier: a data object that does not comply with the general

behavior of the data

y It can be considered as noise or exception but it is quite

useful in fraud detection, rare events analysisy The analysis of outlier data is referred to as outlier analysis

or anomaly mining.



Are all Patterns Interesting?

y What makes a pattern interesting?

y Can a data mining system generate all of the interesting

patterns?

yCan a data mining system generate only interesting patterns?



Cont«

y A data mining system/query may generate thousands of patterns, not all of them are

interesting.

y Suggested approach: Human-centered, query-based, focused mining

y

Interestingness measures: A pattern is interesting if it is easily understood byhumans, valid on new or test data with some degree of certainty, potentially useful,

novel, or validates some hypothesis that a user seeks to confirm

y Objective vs. subjective interestingness measures:

yObjective: based on statistics and structures of patterns, e.g., support,confidence, etc.

y Subjective: based on user·s belief in the data, e.g., unexpectedness, novelty,

actionability, etc.



Can We Find All and Only Interesting

Patterns?

y Find all the interesting patterns:Completeness

y Can a data mining system find all the interesting patterns?

y Association vs. classification vs. clustering

y Search for only interesting patterns: Optimization

y Can a data mining system find only the interesting patterns?

y Approaches

y First general all the patterns and then filter out the uninteresting ones.

y Generate only the interesting patterns³mining query optimization



Data Mining : Classification Schemes

y General Functionality

y Descriptive data mining

y Predictive data mining

y

Different views, different classificationsy Kinds of databases to be mined

y Kinds of knowledge to be discovered

y Kinds of techniques utilized

y

Kinds of applications adapted



A Multi-Dimensional View of Data

Mining Classificationy Databases to be mined

y Relational, transactional, object-oriented, object-relational, active,

spatial, time-series, text, multi-media, heterogeneous, legacy,

WWW, etc.

y Knowledge to be mined

y Characterization, discrimination, association, classification,

clustering, trend, deviation and outlier analysis, etc.

y Multiple/integrated functions and mining at multiple levels



Cont«

y Techniques utilized

y Database-oriented, data warehouse (OLAP), machine learning,

statistics, visualization, neural network, etc.

y

Applications adaptedy Retail, telecommunication, banking, fraud analysis, DNA

mining, stock market analysis,Web mining,Weblog analysis,

etc.



Data Mining : Task Primitives

y Data mining without user interaction is usually not helpful

y Users may request a few ´data mining primitivesµ to be

performed on data

y

specification of data to be minedy set of data in which the user is interested

y kinds of knowledge to be mined

y background knowledge useful in guiding the discovery process

y

specification of how knowledge should be visualized



Pieces of a Data Mining Tasky What data to mine

y list of relevant attributes

y Kinds of knowledge to be minedy characterizationy discriminationy associationy classificationy clusteringy evolution analysis

y Background knowledgey concept hierarchies

y Interestingness Measuresy separate patterns from knowledge

y Presentation and visualization of patterns



Task Relevant Data

y Mixable ´viewµ of the data

y name of database or warehouse

y name of tables or cubes

y

conditions for selecting useful datay type = ́ home entertainmentµ

y type = ́ fruitµ

y attributes or dimensions (e.g.; name and price)



Kind of Knowledge to be Mined

y Templates or ´meta patternsµ may be used to specify output

of results:

y P(X: customer,W) AND Q(X,Y) -> buys(X,Z)

y age(X,µ30..30µ) AND income(X, µ40K«49Kµ) -> buys(X, ́

VCRµ) [2.2%, 60%]

y Might specify to classify input file of customers as ´ likely to buy

µ, ´ not likely to buyµ

y indicates 60% confidence is to be used and such cases should

represent 2.2% of all transactions.



Background Knowledge:

Concept Hierarchies

y Concept Hierarchy

y defines a sequence of mappings from a set of low-level

y concept to higher-level.

y location

y time

y product

y Types of hierarchies

y schema hierarchy

y set-grouping hierarchy

y operation derived hierarchy

y rule-based hierarchy



Concept Hierarchies

y Schema

y total or partial order among an attribute, usually aware house

dimension (time, location, etc.)

y

Set-Groupy values for a given attribute are lumped into groups of constants

or range values

y Operation defined

y automatically derived, clustering, extraction, etc.

y Rule-based

y hierarchy may be well defined by set of rules



Interestingness Measures

y Simplicity

e.g., (association) rule length, (decision) tree size

y Certainty

e.g., confidence, P(A|B) = n(A and B)/ n (B), classificationreliability or accuracy, certainty factor, rule strength, rule quality,discriminating weight, etc.

y Utility

potential usefulness, e.g., support (association), noise threshold

(description)y Novelty

not previously known, surprising (used to remove redundantrules, e.g.,Canada vs. Vancouver rule implication support ratio



Presentation and visualization of patterns

y Different backgrounds/usages may require different forms of

representation

y E.g., rules, tables, crosstabs, pie/bar chart etc.

yConcept hierarchy is also importanty Discovered knowledge might be more understandable when

represented at high level of abstraction

y Interactive drill up/down, pivoting, slicing and dicing provide

different perspective to data

y Different kinds of knowledge require different representation:

association, classification, clustering, etc.



Thank you!!!

Data Mining - Functionalities,Classification and Task Primitives

Documents