Merging Mixture Components for Clustering Through Pairwise Overlap. Issue 1 (2nd January 2016) - Melnykov, Volodymyr
Abstract : Finite mixture models are well known for their flexibility in modeling heterogeneity in data. Model-based clustering is an important application of mixture models, which assumes that each mixture component distribution can adequately model a particular group of data. Unfortunately, when more than one component is needed for each group, the appealing one-to-one correspondence between mixture components and groups of data is ruined and model-based clustering loses its attractive interpretation. Several remedies have been considered in literature. We discuss the most promising recent results obtained in this area and propose a new algorithm that finds partitionings through merging mixture components relying on their pairwise overlap. The proposed technique is illustrated on a popular classification and several synthetic datasets, with excellent results.