ABSTRACT
This chapter presents a method for clustering that is based on a finite mixture probability model. The model-based clustering methodology is based on probability models, such as the finite mixture model for probability densities. The idea of using probability models for clustering has been around for many years. The finite mixture approach to probability density estimation can be used for both univariate and multivariate data. The problem of estimating the parameters in a finite mixture has been studied for many years. The approach is called the Expectation-Maximization (EM) method. This is a general method for optimizing likelihood functions and is useful in situations where data might be missing or simpler optimization methods fail. The Bayes approach to pattern classification is a fundamental technique, and is recommended as the starting point for most pattern recognition applications. The chapter describes another approach to clustering that is based on estimating a finite mixture probability density function.
