Fuzzy clustering algorithm for latent class model

被引：4
|
作者：
Lin, CT [1 ]
Chen, CB
Wu, WH
机构：
[1] Ming Chuan Univ, Grad Sch Management, Taipei, Taiwan
[2] Natl Dong Hwa Univ, Dept Int Business, Hualien, Taiwan
[3] Yuanpei Univ Sci & Technol, Dept Healthcare Management, Hsinchu, Taiwan
来源：
STATISTICS AND COMPUTING | 2004年 / 14卷 / 04期
关键词：
fuzzy clustering; latent class model; EM algorithm; maximum penalized likelihood; penalty fuzzy c-means;
D O I：
10.1023/B:STCO.0000039479.56180.d5
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
The expectation maximization (EM) algorithm is a widely used parameter approach for estimating the parameters of multivariate multinomial mixtures in a latent class model. However, this approach has unsatisfactory computing efficiency. This study proposes a fuzzy clustering algorithm (FCA) based on both the maximum penalized likelihood (MPL) for the latent class model and the modified penalty fuzzy c-means (PFCM) for normal mixtures. Numerical examples confirm that the FCA-MPL algorithm is more efficient (that is, requires fewer iterations) and more computationally effective (measured by the approximate relative ratio of accurate classification) than the EM algorithm.
引用
收藏
页码：299 / 310
页数：12
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