Determining the number of clusters in cluster analysis

My Young Cheong, Hakbae Lee

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


Cluster analysis has been a popular method for statistical classification. The classical cluster analysis, however, has a theoretical shortcoming in the sense that the inference to determine the number of clusters does not provide the theoretical guideline. To estimate the number of clusters, this paper explores the problem through the EM algorithm, Maximum a Posteriori and Gibbs sampler. In addition, we investigate the Bayesian Information criteria (BIC), the Laplace Metropolis criteria and the modified Fisher's criteria in order to determine the number of clusters.

Original languageEnglish
Pages (from-to)135-143
Number of pages9
JournalJournal of the Korean Statistical Society
Issue number2
Publication statusPublished - 2008 Jun

Bibliographical note

Funding Information:
This research was supported by Fund for Supporting Basic Science Research in the College of Business and Economics, Yonsei University.

All Science Journal Classification (ASJC) codes

  • Statistics and Probability


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