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Fuzzy clustering algorithm for latent class model |
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| Authors: | Lin Chin-Tsai Chen Chie-Bein Wu Wen-Hsiang |
| Institution: | (1) Graduate School of Management, Ming Chuan University, Taipei, Taiwan, R.O.C;(2) Department of International Business, National Dong Hwa University, Hualien, Taiwan, R.O.C;(3) Department of Healthcare Management, Yuanpei University of Science and Technology, Hsinchu, Taiwan, R.O.C |
| 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. |
| Keywords: | fuzzy clustering latent class model EM algorithm maximum penalized likelihood penalty fuzzy c-means |
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