Using Bayesian Classifiers to Enhance Clustering
Weihong Wang, Qu Li, Shanshan Han, Xuezhi Zheng · 2006
Recently, combining Naive-Bayes with the expectation maximization (EM) algorithm for unsupervised learning have received significant attention. AutoClass is a classical Bayesian clustering algorithm that uses Naive-Bayes in combination with EM algorithm to find the probability distribution parameters to best fit the data. In this study, we introduce a robust approach, which is similar to AutoClass, it can arbitrarily impose any Bayesian classifiers in combination with EM algorithm to enhance cluster's performance. This paper focuses on how clustering techniques can benefit from classification. We provide experimental evidence that more accurate than original results of clustering in the t-test on most of the benchmark data sets