Determining the number of clusters in cluster analysis

My Young Cheong; Hakbae Lee

doi:10.1016/j.jkss.2007.10.004

Determining the number of clusters in cluster analysis

My Young Cheong, Hakbae Lee

Department of Applied Statistics

Research output: Contribution to journal › Article › peer-review

6 Citations (Scopus)

Abstract

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 language	English
Pages (from-to)	135-143
Number of pages	9
Journal	Journal of the Korean Statistical Society
Volume	37
Issue number	2
DOIs	https://doi.org/10.1016/j.jkss.2007.10.004
Publication status	Published - 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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10.1016/j.jkss.2007.10.004

Cite this

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

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AB - 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.

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Determining the number of clusters in cluster analysis

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

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