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Mathematical optimization methods for clustering and classification with biological and medical applications

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TitleInfo
Title
Mathematical optimization methods for clustering and classification with biological and medical applications
Name (type = personal)
NamePart (type = family)
Chou
NamePart (type = given)
Chun-An
DisplayForm
Chun-An Chou
Role
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author
Name (type = personal)
NamePart (type = family)
Chaovalitwongse
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Wanpracha Art
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Wanpracha Art Chaovalitwongse
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Advisory Committee
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chair
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Boros
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Endre
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Endre Boros
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Advisory Committee
Role
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internal member
Name (type = personal)
NamePart (type = family)
Jeong
NamePart (type = given)
Myong-K.
DisplayForm
Myong-K. Jeong
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Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Pham
NamePart (type = given)
Hoang
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Hoang Pham
Affiliation
Advisory Committee
Role
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outside member
Name (type = personal)
NamePart (type = family)
Berger-Wolf
NamePart (type = given)
Tanya Y.
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Tanya Y. Berger-Wolf
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Advisory Committee
Role
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outside member
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Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
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Graduate School - New Brunswick
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Text
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theses
OriginInfo
DateCreated (qualifier = exact)
2011
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2011-10
CopyrightDate (qualifier = exact)
2011
Place
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xx
Language
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eng
Abstract (type = abstract)
The focus of the dissertation is on the development of effective combinatorial optimization approaches for both large-scale clustering and classification problems in data mining with high computational complexity by massive biological and medical data. In the first part, we study an important clustering problem in computational and population biology, namely sibling reconstruction problem. The problem is mathematically considered a special case of capacitated clustering problem. A mathematical optimization model is proposed to establish the sibling relationships (i.e., groups of siblings) based on the biological concept of combinatorial constraints and similarity likelihood of genetic data. Both exact and heuristic solution approaches are developed, which enable the problem to be solved comparably and outperform other existing combinatorial and statistical approaches significantly. In the second part, we develop new combinatorial and pattern-based optimization approaches in the framework of Logical Analysis of Data (LAD) for binary classification. In the framework, while patterns are the building blocks for the LAD classification model, a new mathematical optimization model is proposed for generating decisive and high-quality patterns. Moreover, a column generation framework, where the proposed pattern generation approach is employed, is developed to build an “optimal” LAD classifier such that the classification accuracy and computational efficiency are improved. In the third part, we investigate feature selection that has two-fold advantages in classification problems with massive data: data reduction and noise reduction. First, we formulate a quadratic program by using statistical information (relevancy and redundancy) of features as inputs to select critical features that are favorable for classifiers. Second, we propose a new pattern-based optimization approach using a decomposed nearest neighbor rule for direct classification. The preliminary results show the potential for the improvement in data reduction and classification accuracy.
Subject (authority = RUETD)
Topic
Industrial and Systems Engineering
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Title
Rutgers University Electronic Theses and Dissertations
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ETD
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ETD_3595
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electronic resource
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application/pdf
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Extent
xv, 137 p. : ill.
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Note (type = vita)
Includes vita
Note (type = statement of responsibility)
by Chun-An Chou
Subject (authority = ETD-LCSH)
Topic
Combinatorial optimization
Subject (authority = ETD-LCSH)
Topic
Mathematical optimization
Identifier (type = hdl)
http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000063358
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Graduate School - New Brunswick Electronic Theses and Dissertations
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rucore19991600001
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Identifier (type = doi)
doi:10.7282/T32B8X5M
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

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The author owns the copyright to this work.
RightsHolder (type = personal)
Name
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Chou
GivenName
Chun-An
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RightsEvent
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Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2011-09-26 14:14:16
AssociatedEntity
Name
Chun-An Chou
Role
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Affiliation
Rutgers University. Graduate School - New Brunswick
AssociatedObject
Type
License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
RightsEvent
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2011-10-31
DateTime (encoding = w3cdtf); (qualifier = exact); (point = end)
2012-05-01
Type
Embargo
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after May 1st, 2012.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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