Tuesday, Jan 19, 2016 - 3:30pm - Allen 14
Merging mixture components for clustering
Dr. Volodymyr Melnykov, Statistics, University of Alabama
Title: Merging mixture components for clustering
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 that 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. Extensions of the developed technique are considered in the context of clustering large datasets.