DM and Visualization SJBIT 19 07 10

8/2/2019 DM and Visualization SJBIT 19 07 10

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Visualization and Data Mining

Lecture delivered

byDr S.Natarajan, Professor ISE, PESIT

forFDPAt

SJBIT Bangaloreon19-7-2010



What is Visualization?

• Dictionary (Merriam-Webster Online Dictionary)

• Wikipedia



March 30, 2012 Data Mining: Concepts and Techniques 3

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



What is Visualization?

• NSF (1987)

“Visualization is a method of computing. Ittransforms the symbolic into the geometric,enabling researchers to observe theirsimulation and computations. Visualization

offers a method to see the unseen.”



Function of Visualization

• Support reasoning (process, calculate, reason)

– For analysing, interpreting the data

• Convey information (share, persuade,

collaborate, emphasize) – Communicating between individuals and groups

– Present, Disseminate Information, Results

• Archive record – Visually memorize through photography,

drawings, etc.



Rationale To Understand Visualization

• Computer and Computer graphics

digitalinformation

interpretationmeasurements

digitalrepresentation

Transform(Visualization)

Knowledge

http://images.google.co.nz/imgres?imgurl=http://www.visioncareohio.com/VCO-image/eye.gif&imgrefurl=http://www.visioncareohio.com/VCO-list.htm&usg=__ESMdWogAiOfNc5c6jYmQIuTSU00=&h=352&w=480&sz=16&hl=en&start=78&um=1&tbnid=_e1kf8BA7ZS1RM:&tbnh=95&tbnw=129&prev=/images%3Fq%3Deye%26start%3D60%26ndsp%3D20%26um%3D1%26hl%3Den%26sa%3DN

http://images.google.co.nz/imgres?imgurl=http://www.physics.uoguelph.ca/psi/icons/LEO.jpg&imgrefurl=http://www.physics.uoguelph.ca/psi/cryotem.shtml&usg=__eq8-C-dn5_uvfREfek1MJb6ZAiU=&h=334&w=294&sz=85&hl=en&start=4&um=1&tbnid=gFzawbkUNMfWTM:&tbnh=119&tbnw=105&prev=/images%3Fq%3Delectronic%2Bmicroscope%26um%3D1%26hl%3Den%26sa%3DG



Jacques Bertin -- Semiology



Rationale To Understand Visualization

• More Data, More Information

• More Complex, Large Volume of Data

digital

information

interpretationmeasurement

digital

representation

transform

digitalinformation

digitalInformation

digitalrepresentation

digital

representation

digitalRepresentation

measurement

simulation

Knowledge



http://images.google.co.nz/imgres?imgurl=http://www.noaanews.noaa.gov/stories2007/images/supercomputer-banks-noaa2.jpg&imgrefurl=http://www.noaanews.noaa.gov/stories2007/s2785.htm&usg=__UB2RHNauZvFOFaP6B4EXbOf00_4=&h=782&w=1200&sz=295&hl=en&start=2&um=1&tbnid=MBGwdr9lOSEHIM:&tbnh=98&tbnw=150&prev=/images%3Fq%3Dsupercomputer%26um%3D1%26hl%3Den%26sa%3DG

http://images.google.co.nz/imgres?imgurl=http://nidandiagnostics.com/images/nid_img/ct_scan.jpg&imgrefurl=http://nidandiagnostics.com/spiral_ct_scan.aspx&usg=__b6V59fGnw7qsQ-q9U1Y3F-rrOSc=&h=377&w=476&sz=20&hl=en&start=19&um=1&tbnid=tdyJNFKVRHIrDM:&tbnh=102&tbnw=129&prev=/images%3Fq%3Dct-scan%26um%3D1%26hl%3Den%26sa%3DG





http://images.google.co.nz/imgres?imgurl=http://www.physics.uoguelph.ca/psi/icons/LEO.jpg&imgrefurl=http://www.physics.uoguelph.ca/psi/cryotem.shtml&usg=__eq8-C-dn5_uvfREfek1MJb6ZAiU=&h=334&w=294&sz=85&hl=en&start=4&um=1&tbnid=gFzawbkUNMfWTM:&tbnh=119&tbnw=105&prev=/images%3Fq%3Delectronic%2Bmicroscope%26um%3D1%26hl%3Den%26sa%3DG



Different Types of Visualization

• Scientific Visualization (SciViz)

– Measured Data

– Simulated Data

Example: weather, CFD

• Information Visualization (InfoViz)

– Abstract Data

Example: population, files on your PC

http://www.cs.umd.edu/hcil/millionvis/census2k.gif

http://www.cs.umd.edu/hcil/millionvis/million-treemap.gif


http://slidepdf.com/reader/full/dm-and-visualization-sjbit-19-07-10 10/99March 30, 2012 Data Mining: Concepts and Techniques 10

Visual Data Mining: An Overview

What is Visual Data Mining?

Survey of techniques

Data Visualization

Visualizing Data Mining Results

Visual Data Mining



What Is Visual Data Mining?

Visual data mining “discovers implicit and usefulknowledge from large data sets using data

and/or knowledge visualization techniques”

Data visualization + Data mining techniques



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 – +



Visualization Model

Visualization pipeline (Data and Process)

Data Oriented Process



Visualization Model

Visualization pipeline: human in the loop



Visualization Research Field

The Visualization Field emerged as computers gotfaster and had better graphics

Need for processing large amount of data

Change in computing from symbol based to graphics based

1990 First Conference (Eurographics Viz, IEEE Viz)

1995 First Journal (Transaction on Visualization andComputer Graphics)



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



Scope of Visual Data Mining

Visualization: Use of computer graphics to create visualimages which aid in the understanding of complex, oftenmassive representations of data

Visual Data Mining: The process of discovering implicit butuseful knowledge from large data sets using visualization

techniques

ComputerGraphics

HighPerformanceComputing

PatternRecognition

HumanComputer

Interfaces

MultimediaSystems



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



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



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.



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



Motivation for Visualization

Find the number of E‟s in the following text:

TYUKSAWHZWESC

GHEKVXAISXBLSE JUIDVRTHOXEWPX



Motivation for Visualization

Find the number of E‟s:

oA IE

5

4

3

2

1



3 D Wireframe lattice




19901998 2000 2002

Expectations

Performance

The Hype Curve for

Data Mining and Knowledge Discovery

Over-inflatedexpectations

Disappointment

Growing acceptanceand mainstreaming

risingexpectations




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






30

Chernoff Faces

Encode different variables‟ values in characteristics of human face. The user decides its own coding.

http://www.cs.uchicago.edu/~wiseman/chernoff/

http://hesketh.com/schampeo/projects/Faces/chernoff.html Cute applets:






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]



High Resolution Satellite data





Visualization Principle• From Data to Visual Representation

Visualization: Convey Information through VISUAL representations to explore, analyze, discover, explainsparse/dense, complex, multidimensional DATAIt shows a variety of activities including incoming calls,outgoing calls both answered, and unanswered. It also shows incoming and outgoing SMS messages. Theapplication takes this data and creates a visual map of the day on your phone. While this may not be the firstcell phone inherent visualization - it's the first one I've seen. I would love to see this for more phone platforms.

Currently, it's only available for Symbian based mobile phones.

101210323123213434324

SADAS

11’ 33’ 4

DATA VISUAL REPRESENTATION

TRANSFORMGENERATE

http://www.maxkiesler.com/index.php/vizlist/fullview/today_visualize_your_cell_phone_activity

http://images.google.co.nz/imgres?imgurl=http://www.childmindingbysara.co.uk/images/bigstockphoto_Toddler_Cellphone_Call_4968.jpg&imgrefurl=http://www.childmindingbysara.co.uk/contact.html&usg=__cEu7Q57AL0oHPOLKGo2AAZ-Odow=&h=600&w=800&sz=305&hl=en&start=107&um=1&tbnid=J2bHasbWGJA_SM:&tbnh=107&tbnw=143&prev=/images%3Fq%3Dcall%2Bcellphone%26start%3D100%26ndsp%3D20%26um%3D1%26hl%3Den%26lr%3D%26sa%3DN

http://wwwdelivery.superstock.com/WI/223/1560/PreviewComp/SuperStock_1560R-2050552.jpg





http://www.visual-literacy.org/periodic_table/periodic_table.html







Selective Dynamic Manipulation(SDM) Sample

• SDM deals with these issues

• Selection and control methods rely on objectsrather than spaces.





SDM Feedback Mechanisms• Clearly Identify the Selected

Set – The selected set is painted

differently (green)

– A white grid may be drawn

beneath all selected objects to

indicate positions and spread

• Maintain Scene Context

– Used to maintain context

when objects are displaced

– Object ‘body’ (green) andobject ‘shell’ (white)





A Brief History of Visualization

• 105-150

– Invention of Paper + Ptolemy World Map

[from http://www.math.yorku.ca/SCS/Gallery/milestone/]




• 1453-1500

– Guttenberg+ Arnovalley (Da Vinci)







• 1600-1850 – Printed map, weather map+ contour techniques, pie,

bar charts + first photographical technique




A Brief History of Visualization• 1850-1974

– Motion, color photography, cinematographe, stereograme, psychometrics,

computer visualization, semiology


SYMAP, producing isoline, choropleth and proximal maps on a line printer- HowardFisher , USASemiology: study of signs and symbols




• 1974-Present – More techniques, 3D, interactivity, animation


Historical Overview of Exploratory

http://www.research.att.com/areas/stat/xgobi/xgobi-example.gif




Historical Overview of ExploratoryData Visualization Techniques (cf. [WB 95])

Pioneering works of Tufte [Tuf 83, Tuf 90] and Bertin [Ber81] 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



Visualization and Fields

• Multi-disciplinary

Visualization

DigitalImaging

ParallelComputing

Perception

Cognitive

Algorithmic

ArtComputerGraphics

Electronic



51 J.C.Roberts

Minard’s plot

http://www.math.yorku.ca/SCS/Gallery/re-minard.html

The French engineer, Charles Minard (1781-1870), illustrated the

disastrous result of Napoleon's failed Russian campaign of 1812.



52 J.C.Roberts

One of the first uses of a map todisplay epidemiological data was

this dot chart (from Tufte, 1983, p.

24) by Dr. John Snow (1855)

showing deaths from cholera (dots)

in relation to the locations of publicwater pumps.

Tufte says, "Snow observed that

cholera occurred almost entirely

among those who lived near (and

drank from) the Broad Street water

pump. He had the handle of the

contaminated pump removed, ending

the neighborhood epidemic which

had taken more than 500 lives."

S ‟ Ch l

http://umdas.med.miami.edu/aha/displays/gallery/port_sno.htm

http://umdas.med.miami.edu/aha/displays/gallery/port_sno.htm



53

Snow‟s CholeraMap, 1855



54

Napoleon Invasion of Russia, 1812

Napoleon



55

Marley, 1885





Baseball data:



Scatterplots and histograms and bars (from Wills 95)

select high

salaries

avg career

HRs vs avg

career hits

(batting ability)

avg assists vs

avg putouts

(fielding ability)

how long

in majors

distribution

of positions

played

[www.sims.berkeley.edu/courses/is247/s02/lectures/Lecture3.ppt]

Li ki t f i t b h i



Linking types of assist behaviorto position played (from Wills 95)

[www.sims.berkeley.edu/courses/is247/s02/lectures/Lecture3.ppt]




Landscapes [Wis 95]

• Visualization of the data as perspective landscape

• The data needs to be transformed into a (possibly artificial) 2D spatialrepresentation which preserves the characteristics of the data

news articlesvisualized as

a landscape

U s e d b y p e r m i s s i o n o f B .

W r i g h t , V i s i b l e D e c i s i o n s I n c .



61

Parallel Coordinates

• Encode variables along a horizontal row• Vertical line specifies values

Dataset in a Cartesian coordinates

Same dataset in parallel coordinates

Invented by Alfred Inselberg

while at IBM, 1985





63

The parallel coordinate plot:

• The parallel coordinate plot, described by Al Inselberg (1985), representsmultidimensional data using lines.

• Whereas in traditional Cartesian coordinates all axes are mutuallyperpendicular, in parallel coordinate plots, all axes are parallel to one

another and equally spaced.• In this approach, a point inm-dimensional space is represented as a

series of m-1 line segments in 2-dimensional space. Thus, if the originaldata observation is written as (x1, x2, … xm,), then its parallel coordinaterepresentation is them-1 line segments connecting points (1,x1), (2,x2), . .. (m,xm).

• Typically, features will be standardized before a parallel coordinate plot isdrawn.



Flower and some of its parts

l i li i i



65

Example: Visualizing Iris Data

sepallength

sepalwidth

petallength

petalwidth

5.1 3.5 1.4 0.2

4.9 3 1.4 0.2

... ... ... ...

5.9 3 5.1 1.8

Iris setosa

Iris versicolor

Iris virginica

http://www.missouriplants.com/Bluealt/Iris_virginica_flower.jpg



66

Parallel CoordinatesSepal

Length

5.1

sepallength

sepalwidth

petallength

petalwidth

5.1 3.5 1.4 0.2



67

Parallel Coordinates: 2 DSepal

Length

5.1

SepalWidth

3.5

sepallength

sepalwidth

petallength

petalwidth

5.1 3.5 1.4 0.2



68

Parallel Coordinates: 4 DSepal

Length

5.1

SepalWidth

Petal

length

Petal

Width

3.5

sepallength

sepalwidth

petallength

petalwidth

5.1 3.5 1.4 0.2

1.40.2



69

5.1

3.5

1.4 0.2

Parallel Visualization of Iris data

I B d T h i




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)

Stick Figures




census datashowing age,income, sex,education, etc.

Stick Figures

“two attributes

mapped to axes,

remaining attributes

mapped to angle or

length of limbs”. Look

at texture pattern

Dimensional Stacking




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 theimportant 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

Hierarchical Techniques




Hierarchical Techniques

Basic Idea: Visualization of the data using ahierarchical 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]

Di e io l St ki




Used by permission of M. Ward, Worcester Polytechnic InstituteVisualization of oil mining data with longitude andlatitude mapped to the outer x-, y-axes and ore gradeand depth mapped to the inner x-, y-axes







Disadvantages: Difficult to display more than nine dimensions

Important to map dimensions appropriately

May be difficult to understand visualizations atfirst

C T



Cone Tree

16




Cone Trees



Cone Trees

research.microsoft.com/~ggr/gi97.ppt17

Treemap [JS 91 Shn 92 Joh 93]




Screen-filling method which uses a hierarchical

partitioning of the screen into regions depending on theattribute values

The x- and y-dimension of the screen are partitionedalternately according to the attribute values (classes)

Treemap [JS 91, Shn 92, Joh 93]

MSR Netscan image:



treemaps

Chris Stolte



+ 2003: PhD Dissertation, Stanford

+ Extended the semiology from Bertin & Mackinlay

+ VizQL connected visualizations to databases

+ Accessible drag-and-drop interface

VizQL

Query Data Interpreter Visual Interpreter View

3D GUI for Web Browsing




22





http://research.microsoft.com/ui/TaskGallery/index.htm 23

Web Forager



Web Forager


Web Book



Web Book

research.microsoft.com/~ggr/gi97.ppt 25

3D GUI for Desktop



3D GUI for Desktop




Introduction Approach Related Work PhD Work Next?



October 13, 2006 Nathalie HenryDoctoral Consortium

88

Social Networks

Vizster [Heer 2006]

Infovis Co-authoring Network [Börner et al. 2004]



Presentation at Nowcasting Symposium, Design/Media ArtsDepartment, UCLA, Los Angeles, October 16-17, 2009(original title: “Cultural Analytics annual report 6/2008-9/2009)

Dr. Lev ManovichDirector, Software Studies Initiative, Calit2 + UCSDProfessor, Visual Arts [email protected]

You can capture this lecture using any media and share it.Follow our research: softwarestudies.com

Cultural Visualization techniques

mailto:[email protected]

mailto:[email protected]



cultural analytics research @Software Studies Initiative atCalit2/UCSD - key people:

Dr. Jeremy Douglass | Postdoctoral ResearcherTara Zepel | PhD student, Art HistorySunsern Cheamanunkul | PhD student, Computer Science

So Yamaoka | PhD student, Computer ScienceWilliam Huber | PhD student, Art History

for the expanded list of participants, see softwarestudies.com



Our research is made possible by the support from:

Center for Research in Computing and the Arts (CRCA)California Institute for Telecommunication andInformation (Calit2)

NEH Office of Digital HumanitiesNational Energy Research Scientific Computing CenterSingapore Ministry of EducationBergen University (Norway)

UCHRI

o ware u es n a ve o a ora ors:Yuri Tsivian, Department of Art History, University of Chicago: cinemetrics.lv | filmanalysis



analysis Adele Eisenstein: Digital Formalism project (Department for Theatre, Film and MediaStudies (TFM), Vienna University; the Austrian Film Museum; Interactive MediaSystems Group, Vienna University of Technology) | film analysis

Netherlands Institute for Sound and Vision | television and motion graphicsanalysisSan Diego Museum of Contemporary Art | mapping art for an exhibition Isabel Galhano Rodrigues, University of Porto, Portugal | gesture analysis David Kirsh, Cognitive Science, UCSD | dance video analysis

Ph.D. students, Art History, UCSD | art history and visual culture

Jim Hollan, Cognitive Science, UCSD | visualization | cultural analytics softwareFalko Kuester, Structural Engineering, UCSD + Calit2 | visual analytics | culturalanalytics software Yoav Freund, Computer Science and Engineering, UCSD | machine learning andmachine vision | cultural analytics software Kay O’Halloran, Multimodal Analysis Lab, National University of Singapore | MappingAsian Cultures projectGiorgos Cheliotis: Communication and New Media, National University of Singapore |Mapping Asian Cultures projectMatthew Fuller: Goldsmiths College, University of London | software studies, gamestudies

goals of cultural analytics:- being able to better represent the complexity, diversity,



variability, and uniqueness of cultural processes and artifacts- develop techniques to describe the dimensions of cultural

artifacts and cultural processes which until now received littleor no attention (such as gradual temporal change)- create much more inclusive cultural histories and analysis -ideally taking into account all available cultural objects createdin particular cultural area and time period (“art history without

names”)

- democratize cultural research by creating open-source toolsfor cultural analysis and visualization- create interfaces for exploration of cultural data which

operate across multiple scales - from details of structure of aparticular individual cultural artifact/processes to massivecultural data sets/flows

cultural analytics - typical steps:



-1) description (i.e, “culture into data”):

a) manual: annotation, tagging

b) automatic: software analysis of media; capturing user activity

our focus: easy-to-use techniques for automatic description of visual and

interactive media

2) optional: statistical data analysis

3) data visualization (reduction, summarization) and data mapping

(expansion, outlining, layering)

our focus: new visualization + mapping techniques appropriate for

interactive exploration of large sets of visual objects

4) interpretation (humanities), or explanation (science), or correlation

(social science)



Cultural Analytics software running on HIPerSpace (May 2009)















Example: Home Finder ( Map )



p ( p )

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DM and Visualization SJBIT 19 07 10