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Title: Hierarchical latent class models for cluster analysis
Authors: Zhang, Nevin Lianwen
Keywords: Cluster analysis
Latent class models
Local dependence
Search-based algorithm
Hierarchical latent class models
Issue Date: 2002
Citation: To be published in Journal of Machine Learning Research
Series/Report no.: Computer Science Technical Report ; HKUST-CS02-02
Abstract: Latent class models are used for cluster analysis of categorical data. Underlying such a model is the assumption that the observed variables are mutually independent given the class variable. A serious problem with the use of latent class models, known as local dependence, is that this assumption is often untrue. In this paper we propose hierarchical latent class models as a framework where the local dependence problem can be addressed in a principled manner. We develop a search-based algorithm for learning hierarchical latent class models from data. The algorithm is evaluated using both synthetic and real-world data.
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