Data Mining - Mehmed Kantardzic [262]
(a) Show a bar chart for the variable A.
(b) Show a histogram for the variable B.
(c) Show a line chart for the variable B.
(d) Show a pie chart for the variable A.
(e) Show a scatter plot for A and B variables.
5. Explain the concept of a data cube and where it is used for visualization of large data sets.
6. Use examples to discuss the differences between icon-based and pixel-oriented visualization techniques.
7. Given 7-D samples
(a) make a graphical representation of samples using the parallel-coordinates technique;
(b) are there any outliers in the given data set?
8. Derive formulas for radial visualization of
(a) 3-D samples
(b) 8-D samples
(c) using the formulas derived in (a) represent samples (2, 8, 3) and (8, 0, 0).
(d) using the formulas derived in (b) represent samples (2, 8, 3, 0, 7, 0, 0, 0) and (8, 8, 0, 0, 0, 0, 0, 0).
9. Implement a software tool supporting a radial-visualization technique.
10. Explain the requirements for full visual discovery in advanced visualization tools.
11. Search the Web to find the basic characteristics of publicly available or commercial software tools for visualization of n-dimensional samples. Document the results of your search.
15.8 REFERENCES FOR FURTHER STUDY
Draper, G. M., L. Y. Livnat, R. F. Riesenfeld, A Survey of Radial Methods for Information Visualization, IEEE Transaction on Visualization and Computer Graphics, Vol. 15, No. 5, 2009, pp. 759–776.
Radial visualization, or the practice of displaying data in a circular or elliptical pattern, is an increasingly common technique in information visualization research. In spite of its prevalence, little work has been done to study this visualization paradigm as a methodology in its own right. We provide a historical review of radial visualization, tracing it to its roots in centuries-old statistical graphics. We then identify the types of problem domains to which modern radial visualization techniques have been applied. A taxonomy for radial visualization is proposed in the form of seven design patterns encompassing nearly all recent works in this area. From an analysis of these patterns, we distill a series of design considerations that system builders can use to create new visualizations that address aspects of the design space that have not yet been explored. It is hoped that our taxonomy will provide a framework for facilitating discourse among researchers and stimulate the development of additional theories and systems involving radial visualization as a distinct design metaphor.
Fayyad, V., G. G. Grinstein, A. Wierse, Information Visualization in Data Mining and Knowledge Discovery, Morgan Kaufmann, San Francisco, CA, 2002.
Leading researchers from the fields of data mining, data visualization, and statistics present findings organized around topics introduced in two recent international knowledge-discovery and data-mining workshops. The book introduces the concepts and components of visualization, details current efforts to include visualization and user interaction in data mining, and explores the potential for further synthesis of data-mining algorithms and data-visualization techniques.
Ferreira de Oliveira, M. C., H. Levkowitz, From Visual Data Exploration to Visual Data Mining: A Survey, IEEE Transactions On Visualization And Computer Graphics, Vol. 9, No. 3, 2003, pp. 378–394.
The authors survey work on the different uses of graphical mapping and interaction techniques for visual data mining of large data sets represented as table data. Basic terminology related to data mining, data sets, and visualization is introduced. Previous work on information visualization is reviewed in light of different categorizations of techniques and systems. The role of interaction techniques is discussed, in addition to work addressing the question of selecting and evaluating visualization techniques. We review some representative work on the use of IVT in the context of mining data. This includes both visual-data exploration and visually expressing the outcome