An optimization approach to partitional data clustering

J Kim, J Yang () and S Ólafsson
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J Kim: Korea Small Business Institute(KOSBI)
J Yang: Chonbuk National University
S Ólafsson: Iowa State University

Journal of the Operational Research Society, 2009, vol. 60, issue 8, 1069-1084

Abstract: Abstract Scalability of clustering algorithms is a critical issue facing the data mining community. One method to handle this issue is to use only a subset of all instances. This paper develops an optimization-based approach to the partitional clustering problem using an algorithm specifically designed for noisy performance, which is a problem that arises when using a subset of instances. Numerical results show that computation time can be dramatically reduced by using a partial set of instances without sacrificing solution quality. In addition, these results are more persuasive as the size of the problem is larger.

Keywords: optimization-based partitional clustering; scalability; partitioning (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (2)

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DOI: 10.1057/jors.2008.195

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