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Activity Number: 39 - Topics in Clustering
Type: Contributed
Date/Time: Sunday, July 29, 2018 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #329772 Presentation
Title: Mixture Model Modal Clustering
Author(s): Jose Chacon*
Companies: Universidad De Extremadura
Keywords: mixture model; modal clustering; component merging; mean shift algorithm

The two most extended density-based approaches to clustering are surely mixture model clustering and modal clustering. In the mixture model approach, the density is represented as a mixture and clusters are associated to the different mixture components. In modal clustering, clusters are understood as high density regions separated from each other by zones of lower density. If the true density is indeed in the assumed class of mixture densities, then mixture model clustering allows to scrutinize more subtle situations than modal clustering. However, when mixture modeling is used in a nonparametric way, the correspondence between clusters and mixture components may become questionable. Here we introduce two methods to adopt a modal clustering point of view after a mixture model fit. Examples are provided to illustrate that mixture modeling can also be used for clustering in a nonparametric sense, as long as clusters are understood as the domains of attraction of the density modes. Finally, a simulation study reveals that the new methods are extremely efficient from a computational point of view, while at the same time they retain a high level of accuracy.

Authors who are presenting talks have a * after their name.

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