Machine learning using Bernoulli mixture models: Clustering, rule extraction and dimensionality reduction

Abstract

Probabilistic models are common in the machine learning community for representing and modeling data. In this paper we focus on a probabilistic model based upon Bernoulli mixture models to solve different types of problems in pattern recognition like feature selection, classification, dimensionality reduction and rule generation. We illustrate the effectiveness of Bernoulli mixture models by applying them to various real life datasets taken from different domains, and used as part of various machine learning challenges. Our algorithms, based upon Bernoulli mixture models, are not only simple and intuitive but have also proven to give accurate and good results.