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An extended study of the K-means algorithm for data clustering and its applications

Author

Listed:
  • Ja-Shen Chen

    (Yuan-Ze University)

  • Russell K H Ching

    (California State University)

  • Yi-Shen Lin

    (Chinatrust Commercial Bank)

Abstract

The K-means algorithm has been a widely applied clustering technique, especially in the area of marketing research. In spite of its popularity and ability to deal with large volumes of data quickly and efficiently, K-means has its drawbacks, such as its inability to provide good solution quality and robustness. In this paper, an extended study of the K-means algorithm is carried out. We propose a new clustering algorithm that integrates the concepts of hierarchical approaches and the K-means algorithm to yield improved performance in terms of solution quality and robustness. This proposed algorithm and score function are introduced and thoroughly discussed. Comparison studies with the K-means algorithm and three popular K-means initialization methods using five well-known test data sets are also presented. Finally, a business application involving segmenting credit card users demonstrates the algorithm's capability.

Suggested Citation

  • Ja-Shen Chen & Russell K H Ching & Yi-Shen Lin, 2004. "An extended study of the K-means algorithm for data clustering and its applications," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(9), pages 976-987, September.
    • Handle: RePEc:pal:jorsoc:v:55:y:2004:i:9:d:10.1057_palgrave.jors.2601732
    DOI: 10.1057/palgrave.jors.2601732
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    References listed on IDEAS

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    Cited by:

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    3. J Kim & J Yang & S Ólafsson, 2009. "An optimization approach to partitional data clustering," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(8), pages 1069-1084, August.

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