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Data Mining: Concepts and Techniques, 3rd Edition by Micheline Kamber, Jian Pei, Jiawei Han

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11.2 Clustering High-Dimensional Data

The clustering methods we have studied so far work well when the dimensionality is not high, that is, having less than 10 attributes. There are, however, important applications of high dimensionality. “How can we conduct cluster analysis on high-dimensional data?”

In this section, we study approaches to clustering high-dimensional data. Section 11.2.1 starts with an overview of the major challenges and the approaches used. Methods for high-dimensional data clustering can be divided into two categories: subspace clustering methods (Section 11.2.2 and 11.2.3) and dimensionality reduction methods (Section 11.2.4).

11.2.1 Clustering High-Dimensional Data: Problems, Challenges, and Major Methodologies

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