Merging Mixture Components for Clustering Through Pairwise Overlap. Issue 1 (2nd January 2016)

• Record Type: Journal Article
• Authors: Melnykov, Volodymyr
• Is Part Of: Journal of computational and graphical statistics. Volume 25:Issue 1(2016)
• Page Start: 66
• Page End: 90
• Publication Date: 2016-01-02
• Subjects: BIC -- Entropy -- Finite mixture models -- ICL -- Merging components -- MixSim -- Model-based clustering; Mathematical statistics -- Data processing -- Periodicals; Mathematical statistics -- Graphic methods -- Periodicals; 519.50285
• DOI: 10.1080/10618600.2014.978007
• Languages: English
• ISSNs: 1061-8600

Abstract

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.