Prof. Pier Luca LanziLaurea in Ingegneria InformaticaPolitecnico di MilanoPolo di Milano Leonardo

Tecniche di Apprendimento Automatico per Applicazioni di Data Mining

Visualization Techniquesin Data Mining

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Outline

• Goals of visualization• Advantages• Methodologies• Techniques• User interaction• Problems

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Goals of Data Visualization• Today there is the need to manage a huge

amount of data, and computer systems help us in this task

• Visual Data Mining help to deal with this flood of information, integrating the human in the data analysis process

• Visual Data Mining allows the user to gain insight into the data, drawing conclusions and directly interacting with the data

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Advantages of visualization techniquesThe main advantages of the application of Visualdata mining techniques are:• Visual data exploration can easily deal with very large,

highly non homogeneous and noisy amount of data

• Visual data exploration requires no understanding of complex mathematical or statistical algorithms

• Visualization techniques provide a qualitative overview useful for further quantitative analysis

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Approach methodologies

Confirmative Analysis:• starting point: hypotheses about the data• result: visualization of the data allowing confirmation or rejection of

the hypotheses

Presentation:• starting point: facts to be presented are fixed a priori• result: high-quality visualization of the data presenting the facts

Explorative Analysis:• starting point: data without hypotheses• result: visualization of the data, which can provide hypotheses

about data distribution

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Visualization techniques• Geometric techniques: scatterplots matrices, Hyperslice,

parallel coordinates

• Pixel-oriented techniques: simple line-by-line, spiral and circle segments

• Hierarchical techniques: Treemap, cone trees• Graph-based techniques: 2D and 3D graph• Distortion techniques: hyperbolic tree, fisheye view,

perspective wall• User interaction: brushing, linking, dynamic projections and

rotations, dynamic queries

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Geometric techniques

Basic idea:• Visualization of geometric transformations and

projections of the data

Methods:• Scatterplot matrices• Hyperslice• Parallel coordinates

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Scatterplot matrices

• A scatterplot matrixis composed ofscatter plots of allpossible pairs ofvariables in a dataset

• Assuming a N-dimension dataset,there are (N2-N)/2pairs of twodimension plots

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Hyperslice

• HyperSlice is anextension of thescatterplot matrix

• They represent a multi-dimensionalfunction as amatrix of orthogonal two-dimensionalslices

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Parallel Coordinates• The axes are defined as parallel

vertical lines separated

• A point in Cartesian coordinatescorrespond to a polyline in parallel coordinates

• Able to visualize data that may beoccluded in Cartesian coordinates

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Pixel-oriented techniquesBasic idea:• The basic idea of pixel-oriented techniques is to map each

data value to a colored pixel• Each attribute value is represented by a pixel with a color

tone proportional to a relevance factor in a separate window

Methods:• Simple Arrangement Line-by-Line• Spiral and Circle Segments Techniques

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Pixel-oriented techniques

• Simple arrangement line-by-line

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Pixel-oriented techniques• Spiral

• Circle segments

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Hierarchical techniquesBasic idea:Visualization of the data using a hierarchicalpartitioning into two- or three-dimensionalsubspaces

Methods:• Treemap• Cone trees

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Treemap• Visualization of hierarchical collections of quantitative data as files

on a hard drive, financial analysis, bioinformatics, etc..

• Divide a limited screen space display area into a sequence ofrectangles whose areas correspond to an attribute of data set

http://www.smartmoney.com/marketmap/

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Cone trees3-dimensional extension of the more familiar2-D hierarchical tree structures, to a moreintuitive navigation and display of information

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Graph-based visualization• Graphs (edges + nodes) with labels and

attributes• Used where emphasis is on data relationship

(databases, telecom)• Coordinates not always meaningful• Useful for discovering patterns

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Graph-based visualization• Color and thickness code values• Asymmetric relations:

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Graph-based visualization• E-mail (SeeNet)

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Graph-based visualization• 3D graphs:

– more room for objects– different points of view

• Example (hypertexts – Narcissus):

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Focus vs. context• Too much data in too small screens• Solutions:

– dual views (detailed + global)– distorted view (e.g. fisheye view)

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Distortion• Hyperbolic tree

• Fisheye view

• Perspective wall

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User interaction• Brushing: selecting points or regions• Linking: more views work together

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User interaction• Dynamic projections and rotations

– Interactively and continuously moving through subspaces

• Dynamic queries– Visual interface (button and sliders)– Incremental behavior (undo)

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Problems• Missing attributes

– Ignore – Fill blanks with:

• a predefined constant• a value extracted according to the inferred

distribution

– Assess the effect of interpolated values

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Problems• Large data sets

– Typical screens have one million pixels– Subsampling– Voxel/pixel bins– Jittering

• Large number of attributes– Principal component analysis– Factor analysis– Etc.

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Conclusions• Human and computer skills can be integrated

with visual data mining• Visualization may be useful for:

– understanding what is happening– searching novel patterns

• User interaction is paramount in these

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References (I)• D. A. Keim. “Visual Techniques for Exploring Databases”. Int.

Conference on Knowledge Discovery in Databases, 1997.• D. A. Keim. “Information visualization and visual data mining”. IEEE

Trans. on Visualization and Computer Graphics, jan 2002, vol. 8, no. 1, pp. 1-8

• J. Van Wijk, R. Van Liere. “HyperSlice - Visualization of scalar functions of many variables”. IEEE Visualization, 1993, pp.119-125.

• P. C. Wong, A. H. Crabb, R. D. Bergeron. “Dual multiresolution HyperSlice for multivariate data visualization”. InfoVis 1996

• D. A. Keim. “Pixel-oriented Database Visualizations”. SIGMODRECORD, Special Issue on Information Visualization, 1996.

• M. Ankerst, D. A. Keim, H.-P. Kriegel. “Circle Segments: A Technique for Visually Exploring Large Multidimensional Data Sets”. Visualization '96, 1996.

• B. B. Bederson, B. Shneiderman, M. Wattenberg. “Ordered and Quantum Treemaps: Making Effective Use of 2D Space to Display Hierarchies”. ACM Transactions on Graphics, 2002, pp. 833-854.

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References (II)• R. A. Becker, S. G. Eick, A. R. Wilks. “Visualizing Network Data”.

IEEE Trans. on Visualization and Computer Graphics, mar 1995, vol. 1, no. 1, pp. 16-28

• R. J. Hendley, N. S. Drew, A. M. Wood, R. Beale. “Narcissus: visualising information”. InfoVis 1995, p. 90

• T. A. Keahey, E. L. Robertson (1996). “Techniques for non-linear magnification transformations”. InfoVis 1996

• J. Lamping, R. Rao, P. Pirolli. “A focus+context technique based on hyperbolic geometry for visualizing large hierarchies”. CHI '95, pp. 401-408

• J. D. Mackinlay, G. G. Robertson, S. K. Card. “The perspective wall: detail and context smoothly integrated”. CHI '91, pp. 173-176