Hierarchical Latent Class Models for Cluster Analysis
Nevin L. Zhang; 5(6):697--723, 2004.
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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