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Optimization models and algorithms for sample-preserved classification and clustering

Descriptive

TypeOfResource
Text
TitleInfo (ID = T-1)
Title
Optimization models and algorithms for sample-preserved classification and clustering
Identifier
ETD_2587
Identifier (type = hdl)
http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000053024
Language
LanguageTerm (authority = ISO639-2); (type = code)
eng
Genre (authority = marcgt)
theses
Subject (ID = SBJ-1); (authority = RUETD)
Topic
Industrial and Systems Engineering
Subject (ID = SBJ-2); (authority = ETD-LCSH)
Topic
Classification--Mathematical models
Subject (ID = SBJ-3); (authority = ETD-LCSH)
Topic
Algorithms
Abstract (type = abstract)
This dissertation presents the development of new optimization models and algorithms for sample-preserved classification and clustering. A sample-preserved method keeps some or all of the existing samples when training a rule for classification or clustering, and continues to use them in the testing or predicting phase. Developing a sample-preserved method provides the capability of analyzing time series data due to the largely applied similarity measures on time series. A proposed sample-preserved classification technique, called Support Feature Machine (SFM), finds an optimal combination of features that gives the best classification based on the nearest neighbor rule. It keeps all baseline samples of the selected features in the predicting phase. Variations of SFM models are also presented. In addition, the bilinear program sample-preserved k-median (BPSPKM) clustering algorithm is introduced. While the original k-median problem can be solved by a simple and efficient bilinear program algorithm, it does not have the sample-preserved property, and only works with the 1-norm distance. The sample-preserved k-median (SPKM) clustering method is formulated as an integer programming problem, which is very hard to solve. A bilinear program algorithm is herein proposed in order to obtain local optimal solutions of the SPKM clustering method, as well as a new sequential search algorithm that can solve the SPKM clustering more efficiently. Finally, a novel feature space sample-preserved k-median (FSSPKM) clustering algorithm is proposed, as well as feature selection methods tailor made for such clustering technique. The experimental results show that the original k-median clustering fails to classify time series data due to the lack of the sample-preserved property, and the utilization of time series similarity measures. The sample-preserved medians can avoid having invalid values in some application domains and can be used to represent the samples in the clusters. The BPSPKM clustering algorithm with the Euclidean distance is suggested for clustering attribute (non-time series), univariate time series and multivariate time series data sets. Furthermore, the proposed feature selection methods consider the distances between cluster centers and cluster densities. The results show that the proposed algorithms outperform other feature selection techniques used in the original k-median methods.
PhysicalDescription
Form (authority = gmd)
electronic resource
Extent
xx, 207 p. : ill.
InternetMediaType
application/pdf
InternetMediaType
text/xml
Note (type = degree)
Ph.D.
Note
Includes abstract
Note
Vita
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by Ya-Ju Fan
Name (ID = NAME-1); (type = personal)
NamePart (type = family)
Fan
NamePart (type = given)
Ya-Ju
NamePart (type = date)
1978-
Role
RoleTerm (authority = RULIB)
author
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YA-JU FAN
Name (ID = NAME-2); (type = personal)
NamePart (type = family)
Chaovalitwongse
NamePart (type = given)
Wanpracha Art
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chair
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Advisory Committee
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Wanpracha Art Chaovalitwongse
Name (ID = NAME-3); (type = personal)
NamePart (type = family)
Albin
NamePart (type = given)
Susan
Role
RoleTerm (authority = RULIB)
internal member
Affiliation
Advisory Committee
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Susan Albin
Name (ID = NAME-4); (type = personal)
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Elsayed
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Elsayed A
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internal member
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Advisory Committee
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Elsayed A Elsayed
Name (ID = NAME-5); (type = personal)
NamePart (type = family)
Pham
NamePart (type = given)
Hoang
Role
RoleTerm (authority = RULIB)
internal member
Affiliation
Advisory Committee
DisplayForm
Hoang Pham
Name (ID = NAME-6); (type = personal)
NamePart (type = family)
Boros
NamePart (type = given)
Endre
Role
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outside member
Affiliation
Advisory Committee
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Endre Boros
Name (ID = NAME-1); (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (ID = NAME-2); (type = corporate)
NamePart
Graduate School - New Brunswick
Role
RoleTerm (authority = RULIB)
school
OriginInfo
DateCreated (qualifier = exact)
2010
DateOther (qualifier = exact); (type = degree)
2010
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
RelatedItem (type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier (type = local)
rucore19991600001
Identifier (type = doi)
doi:10.7282/T3M61KBN
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

RightsDeclaration (AUTHORITY = GS); (ID = rulibRdec0006)
The author owns the copyright to this work.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
RightsHolder (ID = PRH-1); (type = personal)
Name
FamilyName
FAN
GivenName
YA-JU
Role
Copyright Holder
RightsEvent (ID = RE-1); (AUTHORITY = rulib)
Type
Permission or license
DateTime
2010-04-13 16:55:02
AssociatedEntity (ID = AE-1); (AUTHORITY = rulib)
Role
Copyright holder
Name
YA-JU FAN
Affiliation
Rutgers University. Graduate School - New Brunswick
AssociatedObject (ID = AO-1); (AUTHORITY = rulib)
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 (ID = RE-2); (AUTHORITY = rulib)
Type
Embargo
DateTime
2010-05-31
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after May 30th, 2012.
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Technical

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ETD
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application/pdf
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application/x-tar
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