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Published in:
2007 | OriginalPaper | Chapter
Visualization of the Clustering Results
Published in: Cluster Analysis for Data Mining and System Identification
Publisher: Birkhäuser Basel
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Since in practical data mining problems high-dimensional data are clustered, the resulting clusters are high-dimensional geometrical objects which are difficult to analyze and interpret. Clustering always fits the clusters to the data, even if the cluster structure is not adequate for the problem. To analyze the adequateness of the cluster prototypes and the number of the clusters, cluster validity measures are used (see Section 1.7). However since validity measures reduce the overall evaluation to a single number, they cannot avoid a certain loss of information. A low-dimensional graphical representation of the clusters could be much more informative than such a single value of the cluster validity because one can cluster by eye and qualitatively validate conclusions drawn from clustering algorithms. This chapter introduces the reader to the visualization of high-dimensional data in general, and presents two new methods for the visualization of fuzzy clustering results.