Partitioning features for model-based clustering using reversible jump MCMC technique

Younghwan Namkoong, Yongsung Joo, Douglas D. Dankel

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In many cluster analysis applications, data can be composed of a number of feature subsets where each is represented by a number of diverse mixture model-based clusters. However, in most feature selection algorithms, this kind of cluster structure has been less interesting because they accounted for discovery of a single informative feature subset for clustering. In this study, we attempt to reveal a feature partition comprising multiple feature subsets, with each represented by a mixture model-based cluster. Searching for the desired feature partition is performed by utilizing a local search algorithm based on a reversible jump Markov Chain Monte Carlo technique.

Original languageEnglish
Title of host publicationProceedings of the 23rd International Florida Artificial Intelligence Research Society Conference, FLAIRS-23
Pages152-153
Number of pages2
StatePublished - 2010
Event23rd International Florida Artificial Intelligence Research Society Conference, FLAIRS-23 - Daytona Beach, FL, United States
Duration: 19 May 201021 May 2010

Publication series

NameProceedings of the 23rd International Florida Artificial Intelligence Research Society Conference, FLAIRS-23

Conference

Conference23rd International Florida Artificial Intelligence Research Society Conference, FLAIRS-23
Country/TerritoryUnited States
CityDaytona Beach, FL
Period19/05/1021/05/10

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