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A methodology for spatial and time series data mining and its applications

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TitleInfo
Title
A methodology for spatial and time series data mining and its applications
Name (type = personal)
NamePart (type = family)
Jeong
NamePart (type = given)
Young-Seon
NamePart (type = date)
1974-
DisplayForm
Youngseon Jeong
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Jeong
NamePart (type = given)
Myong-Kee
DisplayForm
Myong-Kee Jeong
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
chair
Name (type = personal)
NamePart (type = family)
Pham
NamePart (type = given)
Hoang
DisplayForm
Hoang Pham
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Chaovalitwongse
NamePart (type = given)
Wanpracha
DisplayForm
Wanpracha Chaovalitwongse
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Hung
NamePart (type = given)
Ying
DisplayForm
Ying Hung
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
outside member
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
Graduate School - New Brunswick
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (qualifier = exact)
2011
DateOther (qualifier = exact); (type = degree)
2011-05
Place
PlaceTerm (type = code)
xx
Language
LanguageTerm (authority = ISO639-2b); (type = code)
eng
Abstract (type = abstract)
In this dissertation, we present several methodologies for mining spatial and time-sequence data obtained in diverse domains. We first propose a new spatial randomness test and classification method for binary spatial data with specific application to the detection and identification of spatial defect patterns on semiconductor wafer maps. We present the generalized join-count (JC)-based statistic as an alternative approach, and derive a procedure to determine the optimal weights of JC-based statistics. In the proposed methodology, a spatial correlogram, which transforms binary spatial data into time-sequence data, is used as a novel feature to detect spatial autocorrelation and classify spatial defect patterns on the wafer maps. Secondly, we propose a novel distance measure, denoted weighted dynamic time warping (WDTW), for time series classification and clustering problems. The dynamic time warping (DTW) algorithm has been extensively used as a distance measure in combination with the distance-based classifiers. However, the DTW algorithm ignores the relative importance of the phase distance between points in a time series, possibly leading to misclassification. Therefore, we propose a WDTW distance measure which does account for the relative importance of each point in terms of the phase distance between the time series points. Thirdly, we propose a wavelet-based anomaly detection procedure to detect any possible process fault with time-sequence data that have some local variations even under normal working conditions. To handle the large number of parameters in both the mean and variance models, we have developed the wavelet-based mean and variance thresholding procedure to extract a few important wavelet coefficients that may explain local variations in the time domain. Finally, we propose a kernel-based regression with lagged dependent variables. Kernel-based regression techniques are extensively used for exploring the nonlinearity of data in a relatively easy procedure involving the use of various kernel functions. However, the major drawback of current kernel-based regression techniques is their underlying assumption that there is no autocorrelation in the residuals of observations. To avoid this problem, we propose a kernel-based regression model with lagged dependent variables (LDVs), considering autocorrelations of both the response variables and the nonlinearity of data.
Subject (authority = RUETD)
Topic
Industrial and Systems Engineering
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_3177
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
xi, 145 p. : ill.
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Note (type = vita)
Includes vita
Note (type = statement of responsibility)
by Young-Seon Jeong
Subject (authority = ETD-LCSH)
Topic
Data mining--Statistics
Identifier (type = hdl)
http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000061294
RelatedItem (type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier (type = local)
rucore19991600001
Location
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NjNbRU
Identifier (type = doi)
doi:10.7282/T3QC02T0
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Jeong
GivenName
Youngseon
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2011-03-08 15:50:32
AssociatedEntity
Name
Youngseon Jeong
Role
Copyright holder
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.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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