Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Rights and permissions
Copyright information
© 2007 Birkhäuser Verlag AG
About this chapter
Cite this chapter
(2007). Visualization of the Clustering Results. In: Cluster Analysis for Data Mining and System Identification. Birkhäuser Basel. https://doi.org/10.1007/978-3-7643-7988-9_2
Download citation
DOI: https://doi.org/10.1007/978-3-7643-7988-9_2
Publisher Name: Birkhäuser Basel
Print ISBN: 978-3-7643-7987-2
Online ISBN: 978-3-7643-7988-9
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)