The EM algorithm has been used repeatedly to identify latent classes in categorical data by estimating finite distribution mixtures of product components. Unfortunately, the underlying mixtures are not uniquely identifiable and, moreover, the estimated mixture parameters are starting-point dependent. For this reason we use the latent class model only to define a set of "elementary" classes by estimating a mixture of a large number components. We propose a hierarchical "bottom up" cluster analysis based on unifying the elementary latent classes sequentially. The clustering procedure is controlled by minimum information loss criterion. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Grirn, J., & Hora, J. (2007). Minimum information loss cluster analysis for categorical data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4571 LNAI, pp. 233–247). https://doi.org/10.1007/978-3-540-73499-4_18
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