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dc.contributor.advisorHamerly, Gregory James, 1977-
dc.contributor.authorYin, Bing, 1985-
dc.contributor.otherBaylor University. Dept. of Computer Science.en
dc.date.accessioned2009-09-09T13:07:37Z
dc.date.available2009-09-09T13:07:37Z
dc.date.copyright2009-08
dc.date.issued2009-09-09T13:07:37Z
dc.identifier.urihttp://hdl.handle.net/2104/5427
dc.descriptionIncludes bibliographical references (p. 56-58).en
dc.description.abstractWe present an algorithm called HS-means, which is able to learn the number of clusters in a mixture model based on the hierarchical analysis of clustering stability. Our method extends the concept of clustering stability to a concept of hierarchical stability. The method estimates a stable model for the data based on analysis of stability; it then analyzes the stability of each component in the estimated model and chooses a stable model for this component. It continues this recursive stability analysis until all the estimated components are unimodal. In so doing, the method is able to handle data symmetry that existing stability based algorithms have difficulty with. We test our algorithm on both synthetic datasets and real world datasets. The results show that HS-means apparently outperforms existing stability based model selection algorithms and is competitive to other often-used model selection methods.en
dc.description.statementofresponsibilityby Bing Yin.en
dc.format.extentix, 58 p. : ill.en
dc.format.extent1385582 bytes
dc.format.extent72593 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen
dc.rightsBaylor University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. Contact librarywebmaster@baylor.edu for inquiries about permission.en
dc.subjectStability -- Computer programs.en
dc.subjectComputer algorithms.en
dc.subjectCluster analysis -- Computer programs.en
dc.titleHierarchical stability based model selection for clustering algorithms.en
dc.typeThesisen
dc.description.degreeM.S.en
dc.rights.accessrightsWorldwide accessen
dc.contributor.departmentComputer Science.en


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