Cluster Analysis via Stochastic Approximation Monte Carlo

• 발행기관: 한국자료분석학회
• 저자명: Sooyoung Cheon Eunpyo Lee Seohoon Jin
• 간행물 정보: 『Journal of The Korean Data Analysis Society (JKDAS)』Vol.11 No.4, 1749~1760쪽, 전체 12쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2009.08.30

영문 초록

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

1. Introduction

2. The clustering analysis via the SAMC algorithm

3. Numerical results

4. Conclusion

Reference

키워드

k-means clustering Stochastic approximation Monte Carlo Local trap problem Minimum partition error criterion