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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국자료분석학회 Journal of The Korean Data Analysis Society Journal of The Korean Data Analysis Society 제11권 제4호
- 발행연도
- 2009.1
- 수록면
- 1,749 - 1,760 (12page)
이용수
초록· 키워드
오류제보하기
The k-means clustering is one of the simplest unsupervised algorithm used generally in solving clustering problems. However, it may rely on the initial cluster seed and thus it is suffer from the local trap problem due to that its system has multiple local energy minima in a rugged energy landscape. Hence, the global optimal clustering may not be identified. This paper focuses on this problem, and thus we propose to use the Stochastic approximation Monte Carlo(SAMC) algorithm implementing the k-means clustering method to overcome the local trap problem in clustering analysis. SAMC is a general importance sampling and optimization algorithm to search the sample space broadly and escape from the local trap problem regardless of the initial point. The algorithm is tested on simulated and the real dataset, and compared with the k-means clustering algorithm. The numerical results are in favor of SAMC based on the minimization criterion.
목차
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참고문헌 (22)
참고문헌 신청
Atchad´e, Y. F / 2007 / The Wang-Landau algorithm in general state spaces: applications and convergence analysis
Berg, B. A / 1991 / Multicanonical algorithms for 1st order phase-transitions / Physics Letters B : 249 ~ 253
Geyer, C. J / 1991 / Markov chain Monte Carlo maximum likelihood, in Computing Science and Statistics / Proceedings of the 23rd Symposium on the Interface : 156 ~ 163
Geyer, C. J / 1995 / Annealing Markov chain Monte Carlo with applications to ancestral inference / Journal of the American Statistical Association 90 : 909 ~ 920
Hartigan, J. A / 1975 / Clustering Algorithm / John Wiley & Sons
Berg, B. A / 1991 / Multicanonical algorithms for 1st order phase-transitions / Physics Letters B : 249 ~ 253
Geyer, C. J / 1991 / Markov chain Monte Carlo maximum likelihood, in Computing Science and Statistics / Proceedings of the 23rd Symposium on the Interface : 156 ~ 163
Geyer, C. J / 1995 / Annealing Markov chain Monte Carlo with applications to ancestral inference / Journal of the American Statistical Association 90 : 909 ~ 920
Hartigan, J. A / 1975 / Clustering Algorithm / John Wiley & Sons
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