Fuzzy clustering algorithm for latent class model

Authors:
Chin-Tsai Lin;Chie-Bein Chen;Wen-Hsiang Wu
Affiliations:
Graduate School of Management, Ming Chuan University, Taipei, Taiwan, R.O.C. ctlin@mail.yust.edu.tw wenhsiang_wu@yahoo.com.tw;Department of International Business, National Dong Hwa University, Hualien, Taiwan, R.O.C. cbchen@mail.ndhu.edu.tw;Graduate School of Management, Ming Chuan University, Taipei, Taiwan, R.O.C. and Department of Healthcare Management, Yuanpei University of Science and Technology, Hsinchu, Taiwan, R.O.C. ...
Venue:
Statistics and Computing
Year:
2004

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Abstract

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.