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

Ja-Shen Chen (), Russell K H Ching and Yi-Shen Lin
Additional contact information
Ja-Shen Chen: Yuan-Ze University
Russell K H Ching: California State University
Yi-Shen Lin: Chinatrust Commercial Bank

Journal of the Operational Research Society, 2004, vol. 55, issue 9, 976-987

Abstract: 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.

Keywords: data clustering; heuristics; computational analysis; marketing research (search for similar items in EconPapers)
Date: 2004
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://link.springer.com/10.1057/palgrave.jors.2601732 Abstract (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pal:jorsoc:v:55:y:2004:i:9:d:10.1057_palgrave.jors.2601732

Ordering information: This journal article can be ordered from
http://www.springer. ... search/journal/41274

DOI: 10.1057/palgrave.jors.2601732

Access Statistics for this article

Journal of the Operational Research Society is currently edited by Tom Archibald and Jonathan Crook

More articles in Journal of the Operational Research Society from Palgrave Macmillan, The OR Society
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().