Clustering Techniques

Clustering Techniques

Sheng Ma (IBM T.J. Watson Research Center, USA) and Tao Li (Florida International University, USA)
Copyright: © 2005 |Pages: 4
DOI: 10.4018/978-1-59140-557-3.ch034
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Abstract

Clustering data into sensible groupings as a fundamental and effective tool for efficient data organization, summarization, understanding, and learning has been the subject of active research in several fields, such as statistics (Hartigan, 1975; Jain & Dubes, 1988), machine learning (Dempster, Laird & Rubin, 1977), information theory (Linde, Buzo & Gray, 1980), databases (Guha, Rastogi & Shim, 1998; Zhang, Ramakrishnan & Livny, 1996), and bioinformatics (Cheng & Church, 2000) from various perspectives and with various approaches and focuses. From an application perspective, clustering techniques have been employed in a wide variety of applications, such as customer segregation, hierarchal document organization, image segmentation, microarray data analysis, and psychology experiments.

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