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Techniques for Clustering Massive Data Sets

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Clustering and Information Retrieval

Part of the book series: Network Theory and Applications ((NETA,volume 11))

Abstract

The wealth of information embedded in huge databases belonging to corporations (e.g., retail, financial, telecom) has spurred a tremendous interest in the areas of knowledge discovery and data mining. Clustering, in data mining, is a useful technique for discovering interesting data distributions and patterns in the underlying data. The problem of clustering can be defined as follows: given n data points in a d-dimensional metric space, partition the data points into k clusters such that the data points within a cluster are more similar to each other than data points in different clusters.

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Author information

Authors and Affiliations

  1. Department of Computer Information Sciences, University of Pennsylvania, Philadelphia, PA, 19104, USA

    Sudipto Guha

  2. Bell Laboraties, Lucent Technologies, Murray Hill, NJ, 07974, USA

    Rajeev Rastogi

  3. School of Electrical Engineering and Computer Science, Seoul National University, Kwanak P.O. Box 34, Seoul, 151-742, Korea

    Kyuseok Shim

Authors
  1. Sudipto Guha
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  2. Rajeev Rastogi
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  3. Kyuseok Shim
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© 2004 Kluwer Academic Publishers

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Guha, S., Rastogi, R., Shim, K. (2004). Techniques for Clustering Massive Data Sets. In: Clustering and Information Retrieval. Network Theory and Applications, vol 11. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-0227-8_2

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  • DOI: https://doi.org/10.1007/978-1-4613-0227-8_2

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-7949-2

  • Online ISBN: 978-1-4613-0227-8

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