This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
May 2, 2023 Data Mining: Concepts and Techniques
1
Data Mining: Concepts and
Techniques
— Chapter 11 — — Applications and Trends in Data
Mining— Additional Theme: Visual Data Mining
Jiawei Han and Micheline KamberDepartment of Computer Science
University of Illinois at Urbana-Champaignwww.cs.uiuc.edu/~hanj
Disadvantages of human visual system Needs training Not automated Intrinsic bias Limit of about 106 or 107 observations
(Wegman 1995) Power of integration with analytical
methods
May 2, 2023 Data Mining: Concepts and Techniques
7
Scope of Visual Data Mining Visualization: Use of computer graphics to create
visual images which aid in the understanding of complex, often massive representations of data
Visual Data Mining: The process of discovering implicit but useful knowledge from large data sets using visualization techniques
Computer Graphics
High Performance Computing
Pattern Recognition
Human Computer Interfaces
Multimedia Systems
May 2, 2023 Data Mining: Concepts and Techniques
8
Purpose of Visualization Gain insight into an information space by
mapping data onto graphical primitives Provide qualitative overview of large data sets Search for patterns, trends, structure,
irregularities, relationships among data Help find interesting regions and suitable
parameters for further quantitative analysis Provide a visual proof of computer
representations derived
May 2, 2023 Data Mining: Concepts and Techniques
9
Visual Data Mining & Data Visualization
Integration of visualization and data mining data visualization data mining result visualization data mining process visualization interactive visual data mining
Data visualization Data in a database or data warehouse can be
viewed at different levels of abstraction as different combinations of attributes or
dimensions Data can be presented in various visual forms
May 2, 2023 Data Mining: Concepts and Techniques
10
abilities of the computer
General KnowledgeCreativity
Logic
Data Storage
Numerical Computation
Planning
PredictionDiagnosis
Searching
Perception
human abilities
Abilities of Humans and Computers
May 2, 2023 Data Mining: Concepts and Techniques
11
Visual Mining vs. Scientific Vis. & Graphics
Scientific Visualization Often visualize physical model, low
dimensionality Graphics
More concerned with how to render (draw) rather than what to render
May 2, 2023 Data Mining: Concepts and Techniques
12
Data Visualization
View data in database or data warehouse User may control
Different levels of details Subset of attributes
Drawn using boxplots, histograms, polylines, etc.
May 2, 2023 Data Mining: Concepts and Techniques
13
Historical Overview of Exploratory Data Visualization Techniques (cf. [WB 95])
Pioneering works of Tufte [Tuf 83, Tuf 90] and Bertin [Ber 81] focus on Visualization of data with inherent 2D-/3D-semantics General rules for layout, color composition, attribute
mapping, etc. Development of visualization techniques for different
types of data with an underlying physical model Geographic data, CAD data, flow data, image data,
voxel data, etc. Development of visualization techniques for arbitrary
multidimensional data (w.o. an underlying physical model) Applicable to databases and other information
partitioning of the n-dimensional attribute space in 2-dimensional subspaces which are ‘stacked’ into each other
partitioning of the attribute value ranges into classes the important attributes should be used on the outer levels
adequate especially for data with ordinal attributes of low cardinality
attribute 1
attribute 2
attribute 3
attribute 4
May 2, 2023 Data Mining: Concepts and Techniques
28
Used by permission of M. Ward, Worcester Polytechnic InstituteVisualization of oil mining data with longitude and latitude mapped to the outer x-, y-axes and ore grade and depth mapped to the inner x-, y-axes
Dimensional Stacking
May 2, 2023 Data Mining: Concepts and Techniques
29
Dimensional Stacking Disadvantages:
Difficult to display more than nine dimensions
Important to map dimensions appropriately
May be difficult to understand visualizations at first
May 2, 2023 Data Mining: Concepts and Techniques
30
Screen-filling method which uses a hierarchical partitioning of the screen into regions depending on the attribute values
The x- and y-dimension of the screen are partitioned alternately according to the attribute values (classes)
Treemap [JS 91, Shn 92, Joh 93]
MSR Netscan image:
May 2, 2023 Data Mining: Concepts and Techniques
31
May 2, 2023 Data Mining: Concepts and Techniques
32
Treemap of a File System (Schneiderman)
May 2, 2023 Data Mining: Concepts and Techniques
33
Treemaps The attributes used for the partitioning and
their ordering are user-defined (the most important attributes should be used first)
The color of the regions may correspond to an additional attribute
Suitable to get an overview over large amounts of hierarchical data (e.g., file system) and for data with multiple ordinal attributes (e.g., census data)
May 2, 2023 Data Mining: Concepts and Techniques
34
Data Mining Result Visualization
Presentation of the results or knowledge obtained from data mining in visual forms
Examples Scatter plots and boxplots (obtained from
descriptive data mining) Decision trees Association rules Clusters Outliers Generalized rules Text mining
May 2, 2023 Data Mining: Concepts and Techniques
35
Boxplots from Statsoft: Multiple Variable Combinations
May 2, 2023 Data Mining: Concepts and Techniques
36
Visualization of Data Mining Results in SAS Enterprise Miner: Scatter
Plots
May 2, 2023 Data Mining: Concepts and Techniques
37
Visualization of Association Rules in SGI/MineSet 3.0
May 2, 2023 Data Mining: Concepts and Techniques
38
Visualization of Decision Tree in SGI/MineSet 3.0
May 2, 2023 Data Mining: Concepts and Techniques
39
Vizualization of Decision Trees
May 2, 2023 Data Mining: Concepts and Techniques
40
Visualization of Cluster Grouping IBM Intelligent Miner
May 2, 2023 Data Mining: Concepts and Techniques
41
Association Rules (MineSet)
LHS and RHS items are mapped to x-, y-axis
Confidence, support correspond to height of the bar or disc, respectively
Interestingness is mapped to Color
May 2, 2023 Data Mining: Concepts and Techniques
42
MineSet: Association Rules
May 2, 2023 Data Mining: Concepts and Techniques
43
Association Ball Graph (DBMiner)
Items are visualized as balls
Arrows indicate rule implication
Size represents support
May 2, 2023 Data Mining: Concepts and Techniques
44
Classification (SAS EM [SAS 01])
Color corresponds to relative frequency of a class in a node
Branch line thickness is proportional to the square root of the objects
Cluster Form ellipsoids Form blobs(implicit surfaces)
May 2, 2023 Data Mining: Concepts and Techniques
46
H-BLOB
May 2, 2023 Data Mining: Concepts and Techniques
47
Text Mining (ThemeRiver [WCF+ 00])
Visualization of thematic Changes in documents Vertical distance indicates collective strength of the themes
May 2, 2023 Data Mining: Concepts and Techniques
48
Data Mining Process Visualization
Presentation of the various processes of data mining in visual forms so that users can see the flow of data cleaning, integration, preprocessing, mining Data extraction process Where the data is extracted How the data is cleaned, integrated,
preprocessed, and mined Method selected for data mining Where the results are stored How they may be viewed
May 2, 2023 Data Mining: Concepts and Techniques
49
Visualization of Data Mining Processes by Clementine
Understand variations with visualized data
See your solution discovery process clearly
May 2, 2023 Data Mining: Concepts and Techniques
50
Interactive Visual Data Mining
Using visualization tools in the data mining process to help users make smart data mining decisions
Example Display the data distribution in a set of attributes
using colored sectors or columns (depending on whether the whole space is represented by either a circle or a set of columns)
Use the display to which sector should first be selected for classification and where a good split point for this sector may be
May 2, 2023 Data Mining: Concepts and Techniques
51
Visual data mining Projection Pursuits (Class) Tours [Dhillon et al. ’98] Visual Classification [Ankerst et al. KDD