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Efficient clustering of large data sets

Ananthanarayana, VS and Murty, Narasimha M and Subramanian, DK (2001) Efficient clustering of large data sets. In: Pattern Recognition, 34 (12). pp. 2561-2563.

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Clustering is an activity of finding abstractions from data and these abstractions can be used for decision making [1]. In this paper, we select the cluster representatives as prototypes for efficient classification [3]. There are a variety of clustering algorithms reported in the literature. However, clustering algorithms that perform multiple scans of large databases (of size in Tera bytes) residing on the disk demand prohibitive computational times. As a consequence, there is a growing interest in designing clustering algorithms that scan the database only once. Algorithms like BIRCH [2], Leader [5] and Single-pass k-means algorithm [4] belong to this category.

Item Type: Journal Article
Publication: Pattern Recognition
Publisher: Elsevier
Additional Information: Copyright of this article belongs to Elsevier.
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Date Deposited: 16 Mar 2007
Last Modified: 19 Sep 2010 04:36
URI: http://eprints.iisc.ac.in/id/eprint/10437

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