additional theme: visual data mining

May 2, 2023 Data Mining: Concepts and Techniques

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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

©2006 Jiawei Han and Micheline Kamber. All rights reserved.

http://www.cs.uiuc.edu/~hanj


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Visual Data Mining: An Overview

What is Visual Data Mining? Survey of techniques

Data Visualization Visualizing Data Mining Results Visual Data Mining


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What Is Visual Data Mining?

Visual data mining “discovers implicit and useful knowledge from large data sets using data and/or knowledge visualization techniques”

Data visualization + Data mining techniques


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Why Visual Data Mining? Advantages of human visual system

Highly parallel processor Sophisticated reasoning engine Large knowledge base

Can be used to comprehend data distributions, patterns, clusters, and outliers

Data Mining Algorithms

Visualization

Actionable + –Evaluation + –Flexibility – +User Interaction

– +


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Why Not Only Visual Data Mining?

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


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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


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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


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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


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abilities of the computer

General KnowledgeCreativity

Logic

Data Storage

Numerical Computation

Planning

PredictionDiagnosis

Searching

Perception

human abilities

Abilities of Humans and Computers


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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


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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.


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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

resources


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Geometric

Icon-based

Pixel-oriented

Hierarchical

Graph-based

Mapping Projection Filtering Link & Brush Zooming

Simple

Complex

Data Visualization Techniques

Distortion Techniques

Interaction Techniques

Dimensions of Exploratory Data Visualization


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Classification of Data Visualization Techniques

Geometric Techniques: Scatterplots, Landscapes, Projection Pursuit, Prosection Views,

Hyperslice, ParallelCoordinates... Icon-based Techniques:

Chernoff Faces, Stick Figures, Shape-Coding, Color Icons, TileBars,...

Pixel-oriented Techniques: Recursive Pattern Technique, Circle Segments Technique, Spiral- &

Axes-Techniques,... Hierarchical Techniques:

Dimensional Stacking, Worlds-within-Worlds,Treemap, Cone Trees, InfoCube,...

Graph-Based Techniques: Basic Graphs (Straight-Line, Polyline, Curved-Line,...) Specific Graphs (e.g., DAG, Symmetric, Cluster,...) Systems (e.g., Tom Sawyer, Hy+, SeeNet, Narcissus,...)

Hybrid Techniques: arbitrary combinations from above


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Distortion & Dynamic/Interaction Techniques

Distortion Techniques Simple Distortion (e.g. Perspective Wall, Bifocal Lenses,

TableLens, Graphical Fisheye Views,...) Complex Distortion (e.g. Hyperbolic Repr. Hyperbox,...)

Dynamic/Interaction Techniques Data-to-Visualization Mapping (e.g. Auto Visual, S Plus,

XGobi, IVEE,...) Projections: (e.g. GrandTour, S Plus, XGobi,...) Filtering (Selection, Querying) (e.g. MagicLens, Filter/Flow

Queries, InfoCrystal,...) Linking & Brushing (e.g. Xmdv-Tool, XGobi, DataDesk,...) Zooming (e.g. PAD++, IVEE, DataSpace,...) Detail on Demand (e.g. IVEE, TableLens, MagicLens,

VisDB,...)


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Visual Survey

Data visualization techniques Scatterplot Matrices, Landscapes, Parallel

Coordinates Icon-based, Dimensional Stacking, Treemaps


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Direct Visualization

Ribbons w

ith Twists B

ased on Vorticity


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Geometric Techniques Basic Idea

Visualization of geometric transformations and projections of the data

Methods Landscapes [Wis 95] Projection Pursuit Techniques [Hub 85] (a

techniques for finding meaningful projections of multidimensional data)

Scatterplot-Matrices [And 72, Cle 93] Prosection Views [FB 94, STDS 95] Hyperslice [WL 93] Parallel Coordinates [Ins 85, ID 90]


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matrix of scatterplots (x-y-diagrams) of the k-dimensional data [total of (k2/2-k) scatterplots]

Use

d by

erm

issi

on o

f M. W

ard,

Wor

cest

er P

olyt

echn

ic In

stitu

te

Scatterplot-Matrices [Cleveland 93]


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Landscapes [Wis 95]

Visualization of the data as perspective landscape The data needs to be transformed into a (possibly artificial) 2D spatial

representation which preserves the characteristics of the data

news articlesvisualized asa landscape

Use

d by

per

mis

sion

of B

. Wrig

ht, V

isib

le D

ecis

ions

Inc.


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Parallel Coordinates [Ins 85, ID 90] n equidistant axes which are parallel to one of the screen

axes and correspond to the attributes the axes are scaled to the [minimum, maximum]―range of

the corresponding attribute every data item corresponds to a polygonal line which

intersects each of the axes at the point which corresponds to the value for the attribute

Attr. 1 Attr. 2 Attr. kAttr. 3

• • •


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Parallel Coordinates


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Icon-Based Techniques Basic Idea

Visualization of the data values as features of icons Overview

Chernoff-Faces [Che 73, Tuf 83] Stick Figures [Pic 70, PG 88] Shape Coding [Bed 90] Color Icons [Lev 91, KK 94] TileBars [Hea 95]

(use of small icons representing the relevance feature vectors in document retrieval)


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census data showing age, income, sex, education, etc.

used

by

perm

issi

on o

f G. G

rinst

ein,

Uni

vers

ity o

f Mas

sach

uset

tes

at L

owel

l

Stick Figures


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Hierarchical Techniques

Basic Idea: Visualization of the data using a hierarchical partitioning into subspaces.

Overview Dimensional Stacking [LWW 90] Worlds-within-Worlds [FB 90a/b] Treemap [Shn 92, Joh 93] Cone Trees [RMC 91] InfoCube [RG 93]


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Dimensional Stacking [LWW 90]

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


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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


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Dimensional Stacking Disadvantages:

Difficult to display more than nine dimensions

Important to map dimensions appropriately

May be difficult to understand visualizations at first


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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:


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Treemap of a File System (Schneiderman)


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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)


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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


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Boxplots from Statsoft: Multiple Variable Combinations


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Visualization of Data Mining Results in SAS Enterprise Miner: Scatter

Plots


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Visualization of Association Rules in SGI/MineSet 3.0


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Visualization of Decision Tree in SGI/MineSet 3.0


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Vizualization of Decision Trees


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Visualization of Cluster Grouping IBM Intelligent Miner


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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


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MineSet: Association Rules


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Association Ball Graph (DBMiner)

Items are visualized as balls

Arrows indicate rule implication

Size represents support


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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

Tree Viewer


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Cluster Analysis (H-BLOB: Hierarchical BLOB) [SBG 00]

Cluster Form ellipsoids Form blobs(implicit surfaces)


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H-BLOB


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Text Mining (ThemeRiver [WCF+ 00])

Visualization of thematic Changes in documents Vertical distance indicates collective strength of the themes


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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


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Visualization of Data Mining Processes by Clementine

Understand variations with visualized data

See your solution discovery process clearly


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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


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Visual data mining Projection Pursuits (Class) Tours [Dhillon et al. ’98] Visual Classification [Ankerst et al. KDD

’99]


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Projection Pursuits Exploratory projection pursuit:

Goal: reduce dimensionality Define “interestingness” index to each

possible projection of a data set Maximize this index, project linearly Not always possible/useful


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Class Tours “Visualizing Class Structure of

Multidimensional Data” by Dhillon et al. 1998

Problem: Visualize multidimensional data categorized into classes

Solution: Project data into 2D while preserving distances between class means


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Class-Preserving Projection:Preserves distances between projected means


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Tours Tours are animated and interpolated

sequences of 2D projections [Asimov 1985] Class tours: sequences of class-preserving

2-dimensional projections Captures “inter-class structure of complex,

multi-dimensional data”


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Interactive Visual Mining by Perception-Based Classification

(PBC)


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Visual Classification “Visual Classification:

An Interactive Approach to Decision Tree Construction” by Ankerst et al. KDD 99

Exploit expert’s domain knowledge and human visual processing


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Visual Classification


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Visual Classification Results Comparable classification accuracy Can produce more understandable decision

trees Expert domain knowledge can be exploited


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Audio Data Mining Uses audio signals to indicate the patterns of data

or the features of data mining results An interesting alternative to visual mining An inverse task of mining audio (such as music)

databases which is to find patterns from audio data

Visual data mining may disclose interesting patterns using graphical displays, but requires users to concentrate on watching patterns

Instead, transform patterns into sound and music and listen to pitches, rhythms, tune, and melody in order to identify anything interesting or unusual


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Summary Many visualization methods available How to evaluate and compare methods? Need for:

Integrated visualization/exploration systems

Studies of interaction techniques for mining

Practical case studies


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Acknowledgments

Many slides and images from Mihael Ankerst, Boeing, Daniel A. Keim, AT&T, Tutorial at PKDD'2001

Some pictures from Information Visualization in Data Mining and Knowledge Discovery, edited by Usama Fayyad, Georges Grinstein and Andreas Wierse

A good set of slides were prepared by Andrew Wu (Spring 2004)


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additional theme: visual data mining

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