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Hierarchical latent class models for cluster analysis

Authors Zhang, Nevin Lianwen
Issue Date 2002
Source To be published in Journal of Machine Learning Research
Summary 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.
Language English
Format Technical report
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