Events

Tuesday, Jan 19, 2016 - 3:30pm - Allen 14
Statistics seminar
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.

More Events

Upcoming Colloquia, Seminars, and Events