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Fuzzy clustering algorithm for latent class model

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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.

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Lin, CT., Chen, CB. & Wu, WH. Fuzzy clustering algorithm for latent class model. Statistics and Computing 14, 299–310 (2004). https://doi.org/10.1023/B:STCO.0000039479.56180.d5

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