Visualization Techniques in Data Mining

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1 Tecniche di Apprendimento Automatico per Applicazioni di Data Mining Visualization Techniques in Data Mining Prof. Pier Luca Lanzi Laurea in Ingegneria Informatica Politecnico di Milano Polo di Milano Leonardo

2 Outline Goals of visualization Advantages Methodologies Techniques User interaction Problems

3 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

4 Advantages of visualization techniques The main advantages of the application of Visual data 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

5 Approach methodologies Presentation: starting point: facts to be presented are fixed a priori result: high-quality visualization of the data presenting the facts Confirmative Analysis: starting point: hypotheses about the data result: visualization of the data allowing confirmation or rejection of the hypotheses Explorative Analysis: starting point: data without hypotheses result: visualization of the data, which can provide hypotheses about data distribution

6 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

7 Geometric techniques Basic idea: Visualization of geometric transformations and projections of the data Methods: Scatterplot matrices Hyperslice Parallel coordinates

8 Scatterplot matrices A scatterplot matrix is composed of scatter plots of all possible pairs of variables in a dataset Assuming a N-dimension dataset, there are (N 2 -N)/2 pairs of two dimension plots

9 Hyperslice HyperSlice is an extension of the scatterplot matrix They represent a multi-dimensional function as a matrix of orthogonal two-dimensional slices

10 Parallel Coordinates The axes are defined as parallel vertical lines separated A point in Cartesian coordinates correspond to a polyline in parallel coordinates Able to visualize data that may be occluded in Cartesian coordinates

11 Pixel-oriented techniques Basic 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

12 Pixel-oriented techniques Simple arrangement line-by-line

13 Pixel-oriented techniques Spiral Circle segments

14 Hierarchical techniques Basic idea: Visualization of the data using a hierarchical partitioning into two- or three-dimensional subspaces Methods: Treemap Cone trees

15 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 of rectangles whose areas correspond to an attribute of data set

16 Cone trees 3-dimensional extension of the more familiar 2-D hierarchical tree structures, to a more intuitive navigation and display of information

17 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

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

19 Graph-based visualization (SeeNet)

20 Graph-based visualization 3D graphs: more room for objects different points of view Example (hypertexts Narcissus):

21 Focus vs. context Too much data in too small screens Solutions: dual views (detailed + global) distorted view (e.g. fisheye view)

22 Distortion Hyperbolic tree Fisheye view Perspective wall

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

24 User interaction Dynamic projections and rotations Interactively and continuously moving through subspaces Dynamic queries Visual interface (button and sliders) Incremental behavior (undo)

25 Problems Missing attributes Ignore Fill blanks with: a predefined constant a value extracted according to the inferred distribution Assess the effect of interpolated values

26 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.

27 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

28 References (I) D. A. Keim. Visual Techniques for Exploring Databases. Int. Conference on Knowledge Discovery in Databases, 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 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. SIGMOD RECORD, Special Issue on Information Visualization, M. Ankerst, D. A. Keim, H.-P. Kriegel. Circle Segments: A Technique for Visually Exploring Large Multidimensional Data Sets. Visualization '96, 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

29 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 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 J. D. Mackinlay, G. G. Robertson, S. K. Card. The perspective wall: detail and context smoothly integrated. CHI '91, pp