Visual Data Mining: Problems and Applications1 Daniel Keim University of Konstanz Visual Data...

1

Daniel Keim

University of Konstanz

Visual Data Mining:Problems and Applications

Summer School of the Graduiertenkolleg Exploration and Visualization of Large Data Sets

St. Vigil, 16. September 2004

Databases

Visual Data Exploration

Data Mining

MultimediaSim. Search

High-dim. Indexing

Interactive Queries

NN-Queries

Circle Seg.

X-TreeTree Stripping

Cost Models

Denclue OptiGridHD-Eye

InfoFusion

Rec. Pattern

Pixel Bar ChartsVisualPoints

VisDB

CartoDraw

GRADIImage SimSearch

2D Partial SimSearch

Geometric3D SimSearch

VisOpt

Clustering NN-Search in High-dim. DBs

Visual Interfaces

Car Industry

MolecularBiology

Medicine

Image

3D-Objects

CAD

Telcom

Marketing

Monitoring

Cartography

Fraud Detection

Finance

2

Daniel A. Keim: Visual Data Mining3

Overview

1. Introduction

2. Visualization of bibliographic Data 1. InfoVis Contest2. DBLP (Trier Bibliographic Database)

3. Pixel Bar Charts1. Problem2. Solution3. Application

4. Other Problems and Applications1. SOMs of 3D Feature Vectors2. Route Visualization3. E-mail / SPAM Visualization

5. Conclusions

InfoVis Contest

3



4



5


Coauthors of G. Robertson


Coauthors of B. Shneiderman

6


Coauthors of D. Keim

DBLP –Trier Bibliographic Database

7


All Profs

Keim

SaupeReiterer

Berthold

Scholl Waldvogel

Deussen Kuhlen

Leue

Brandes


Prof. U. Brandes

8


Prof. M. Berthold


Prof. R. Kuhlen

9


Prof. O. Deussen


Prof. D. Keim

10


Prof. S. Leue


Prof. H. Reiterer

11


Prof. D. Saupe


Prof. M. Scholl

12


Prof. M. Waldvogel


All Profs

Keim

SaupeReiterer

Berthold

Scholl Waldvogel

Deussen Kuhlen

Leue

Brandes

13


Overview

1. Introduction

2. Visualization of bibliographic Data 1. InfoVis Contest2. DBLP (Trier)



5. Conclusions


Equal-Width Pixel Bar Chart

From Bar Charts to Pixel Bar Charts

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Equal-Height Pixel Bar Chart

Product Type

Product Type


Multi Pixel Bar Charts

1 2 3 4 5 6 7 10 11 12

Color=quantity

Product Type

$ A

mou

nt

Product Type

Color=number of visits

1 2 3 4 5 6 7 10 11 121 2 3 4 5 6 7 10 11 12

Color=dollar amount

Product Type

low

high


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Definition of Pixel Bar Charts

A pixel bar chart is defined by a five tuple

<Dx, Dy, Ox, Oy, C>

where Dx, Dy, Ox, Oy, C ∈ {Al, …, Ak,} and

• Dx / Dy are the dividing attributes on the x-/y-axes

• Ox / Oy are the ordering attributes on the x-/y-axes

• C defines the coloring attribute(s)



Basic Idea of Pixel Bar Charts:

Dividing Attribute on x-Axis (e.g. Product Type)Dx

16




Dividing Attribute on y-Axis (e.g. Region)Dx

Dy




Ordering Attributes on x- and y-Axes

Ox

Oy

C

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Basic Idea of Multi Pixel Bar Charts:

Multi-Pixel Bar Charts

C1 C2

C3 C4




Formalization of the Pixel Positioning Problem:

1. Dense Display Constraint

⎣ ⎦ ),()(:/..1,..1 jidfwithdwpjwi íí =∃=∀=∀

(equal-width Pixel Bar Chart)

⎣ ⎦ ),()(:..1,/..1 jidfwithdhjhpi íí =∃=∀=∀

(equal-height Pixel Bar Chart)

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3. Locality Constraint

min)),1(),,((

))1,(),,((1

1 111

1

1

111

→+

++

∑ ∑∑ ∑

−

= =−−

=

−

=−−

w

x

h

y

w

x

h

y

yxfyxfsim

yxfyxfsim

2. No-Overlap Constraint

)()(:, jiji dfdfjiDBdd ≠⇒≠∈∀



4. Ordering Constraint

xdfxdfaanji jiji ).().(:..1, 11 >⇒>∈∀

ydfydfaanji jiji ).().(:..1, 22 >⇒>∈∀

min2)).,1().,(

).,1().,((

2)).1,().,(

).1,().,((

21

21

1

1 1 21

21

11

11

1

1

1 11

11

→+−

++−

++−

++−

−−

−

= =−−

−−

=

−

=−−

∑ ∑

∑ ∑

ayxfayxf

ayxfayxf

ayxfayxf

ayxfayxf

w

x

h

y

w

x

h

y

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Pixel Placement Algorithm

1. Step:

Determine the -quantiles of Ox

and the - quantiles of Oy.

w

X1 Xw. . .

Y1

Yh

.

.

. h

⎥⎦

⎤⎢⎣

⎡ −w

ww

1,,1

⎥⎦

⎤⎢⎣

⎡ −h

hh

1,,1



2. Step:Position bottom left corner pixel

or{ }⎭⎬⎫

⎩⎨⎧

=∈

−2

1

1 .min|)1,1( addf sXsds { }⎭⎬⎫

⎩⎨⎧

=∈

−1

1

1 .min|)1,1( addf sYsds

Position left and bottom pixels

{ } wiaddif siXsds ..1.min|)1,( 2

1 =∀⎭⎬⎫

⎩⎨⎧

=∈

−

{ } hjaddjf sjYsds ..1.min|),1( 1

1 =∀⎭⎬⎫

⎩⎨⎧

=∈

−

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3. Step:Position remaining pixels

{ } ∅≠∩⎭⎬⎫

⎩⎨⎧

=∩∈

−jYiXifadadsumdjif ss

jYiXsds ).,.(min|),( 211

∅=∩ jYiX

jYXXd iis ∩∪∈ + )( 1

)()( 11 ++ ∪∩∪∈ jjiis YYXXd

If

consider

. . .


Application

Multi-Pixel Bar Chart for Mining 106,199 Customer Buying Transactions(Dx= product type, Dy= ⊥,Ox=dollar amount, Oy=region, C)

Product Type Product TypeProduct Type Product Type

C=Dollar amount C=No. of Visits C=QuantityC= Region

1 2 3 4 5 6 7 10 12 1 2 3 4 5 6 10 12 1 2 3 4 5 6 7 10 12 1 2 3 4 5 6 10 12

low

high

1

2

3

4

5

78

9

10

6

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Application

Multi-Pixel Bar Chart for Mining 405,000 Sales Transaction Records

(Dx= product type, Dy= ⊥, Ox=no. of visits, Oy= dollar amount, C)

customer A$345,000

Product Type Product TypeProduct Type

C=No. of Visits C=QuantityC=Dollar Amount

1 2 3 4 5 6 7 10 12 1 2 3 4 5 6 10 12 1 2 3 4 5 6 7 10 12

low

high

customer A25 visits

customer A500 items


Application

Pixel Bar Charts for mining over 150,000 E-Customer Purchasing Activities (in 1999)

(Dx= month, Dy= ⊥, Ox=no. of visits, Oy= purchase amount, C)

high

customer Ain September customer A

purchase $1,500,000customer Avisits 125times items

customer Apurchase 200items

low

high quantityhigh visits

top dollar amountcustomershighest number of customers

1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12

C=Month C=Purchase Amount C= # of Visits C=Quantity

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Application

Pixel Bar Charts for Mining 106,199 IT Resource Center Customer Activities

IT Resource Center – Search http://itrc.hp.com

(Dx= Search Criteria, Dy= ⊥, Ox=No. of Keywords, Oy= Search Type, C)

Search Criteria

Typ

e

1 2 3 4 1 2 3 4 1 2 3 4

low

high

Search Criteria Search Criteria

fix/solve problem

patch search

C= Search Criteria C= Search Type C= No. of Keywords

search criteria= boolean

type = product search


ApplicationParallel Coordinate Visualization of 150,000 E-Customer Purchasing Activities

Month Purchase Amount Quantity #of Orders Region State Code Month Purchase Amount Quantity #of Orders Region State Code

Focus on one Month (June) Focus on one Purchase Amount (Medium Price)

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Overview

1. Introduction

2. Visualization of bibliographic Data 1. InfoVis Contest2. DBLP (Trier)



5. Conclusions

SOMs of 3D Feature Vectors

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3D-Retrieval on CAD Database

Upper Rows: Combination of 4 Feature VectorsLower Rows: Best Single FV


• Upper Row: Depth Buffer• Middle Row: Silhouette• Lower Row: Linear Combination of Depth Buffer and Silhouette

3D-Retrieval on Internet Database

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3D-Retrieval on Internet Database


Clustering of CAD Database

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Clustering of Internet Database

Route Visualization

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Route Visualization


Route Visualization

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E-mail / SPAM Visualization


E-Mail Visualization

29

Powerwall


30


The EndThe End

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Fachbereich Informatik und Informations-wissenschaften

Datenbanken &Informationssysteme

Algorithmen & Datenstrukturen

Informationswissenschaft

MultimediaSignalverarbeitung

Mensch-ComputerInteraktion

Datenbanken & Visualisierung

Computergrafik &Medieninformatik

BioInformatik & Information Mining

Prof. Dr. D.A.Keim

Prof. Dr. M.Scholl

Prof. Dr. O.Deussen

Prof. Dr. R.Kuhlen

Prof. Dr. U.Brandes

Prof. Dr. D.Saupe

Prof. Dr. H.Reiterer

Prof. Dr. M.R.Berthold

Software Engineering

Computernetze

Prof. Dr. S. Leue

Prof. Dr. M. Waldvogel

Fachbereich Informatik und Informations-wissenschaften

Algorithmen & Datenstrukturen

Informationswissenschaft

Datenbanken & Visualisierung

Computergrafik &Medieninformatik

BioInformatik & Information Mining

Software engineering

Computernetzwerke

Datenbanken &Informationssysteme

MultimediaSignalverarbeitung

Mensch-ComputerInteraktion

Visual Data Mining: Problems and Applications1 Daniel Keim University of Konstanz Visual Data...

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