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Advances in mixture modeling and model based clustering

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dc.contributorAdams, Benjamin Michael
dc.contributorChakraborti, Subhabrata
dc.contributorPorter, Michael D.
dc.contributorWang, Le
dc.contributor.advisorMelnykov, Volodymyr
dc.contributor.authorMichael, Semhar K.
dc.contributor.otherUniversity of Alabama Tuscaloosa
dc.date.accessioned2017-04-26T14:23:41Z
dc.date.available2017-04-26T14:23:41Z
dc.date.issued2015
dc.descriptionElectronic Thesis or Dissertationen_US
dc.description.abstractCluster analysis is part of unsupervised learning that deals with finding groups of similar observations in heterogeneous data. There are several clustering approaches with the goal of minimizing the within cluster variance while maximizing the variance between clusters. K-means or hierarchical clustering with different linkages can be thought as distance-based approaches. Another approach is model-based which relies on the idea of finite mixture models. This dissertations will propose new advances in clustering area mostly related to model-based clustering and its extension to the K-means algorithm. This report has five chapters. The first chapter is a literature review on recent advances in the area of model-based clustering and finite mixture modeling. Main advances and challenges are described in the methodology section. Then some interesting and diverse applications of model-based clustering are presented in the application section. The second chapter deals with a simulation study conducted to analyze the factors that affect complexity of model-based clustering. In the third chapter we develop a methodology for model-based clustering of regression time series data and show its application to annual tree rings. In the fourth chapter, we utilize the relationship between model-based clustering and the Kmeans algorithm to develop a methodology for merging clusters formed by K-means to find meaningful grouping. The final chapter is dedicated to the problem of initialization in model-based clustering. It is well known fact that the performance of model-based clustering is highly dependent on initialization of the EM algorithm. So far there is no method that comprehensively works in all situations. In this project, we use the idea of model averaging and initialization using the emEM algorithm to solve this problem.en_US
dc.format.extent169 p.
dc.format.mediumelectronic
dc.format.mimetypeapplication/pdf
dc.identifier.otheru0015_0000001_0002046
dc.identifier.otherMichael_alatus_0004D_12389
dc.identifier.urihttp://ir.ua.edu/handle/123456789/3018
dc.languageEnglish
dc.language.isoen_US
dc.publisherUniversity of Alabama Libraries
dc.relation.hasversionborn digital
dc.relation.ispartofThe University of Alabama Electronic Theses and Dissertations
dc.relation.ispartofThe University of Alabama Libraries Digital Collections
dc.rightsAll rights reserved by the author unless otherwise indicated.en_US
dc.subjectStatistics
dc.subjectMathematics
dc.titleAdvances in mixture modeling and model based clusteringen_US
dc.typethesis
dc.typetext
etdms.degree.departmentUniversity of Alabama. Department of Information Systems, Statistics, and Management Science
etdms.degree.disciplineApplied Statistics
etdms.degree.grantorThe University of Alabama
etdms.degree.leveldoctoral
etdms.degree.namePh.D.

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