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Adaptive clustering with a variance-aware criterion

Descriptive

TitleInfo
Title
Adaptive clustering with a variance-aware criterion
SubTitle
an alternative to k-means
Name (type = personal)
NamePart (type = family)
Toso
NamePart (type = given)
Rodrigo Franco
NamePart (type = date)
1981-
DisplayForm
Rodrigo Franco Toso
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Kulikowski
NamePart (type = given)
Casimir
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Casimir Kulikowski
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Advisory Committee
Role
RoleTerm (authority = RULIB)
chair
Name (type = personal)
NamePart (type = family)
Elgammal
NamePart (type = given)
Ahmed
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Ahmed Elgammal
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Gerasoulis
NamePart (type = given)
Apostolos
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Apostolos Gerasoulis
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Pardalos
NamePart (type = given)
Panos
DisplayForm
Panos Pardalos
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)
2016
DateOther (qualifier = exact); (type = degree)
2016-05
CopyrightDate (encoding = w3cdtf); (qualifier = exact)
2016
Place
PlaceTerm (type = code)
xx
Language
LanguageTerm (authority = ISO639-2b); (type = code)
eng
Abstract (type = abstract)
This research investigates the effectiveness of a non-convex clustering criterion with the ability to discriminate clusters by means of quadratic boundaries that take into account cluster variances. Since no algorithms have been shown to work efficiently and effectively for this kind of criterion, we introduce and evaluate a generalized version of the incremental one-by-one clustering algorithm of MacQueen (1967) that is suitable for general variance-based criteria, whether convex or not. An experimental evaluation shows that the criterion performs remarkably well with a variety of heterogeneous data sets, both synthetic and real-world. Two novel applications of unsupervised learning to problems in the financial domain are then developed to test the method further. First, given a portfolio of investments with potentially hundreds of holdings, we pose the problem of reducing the number of such holdings while preserving the risk-return characteristics of the original portfolio. Next, we tackle the problem of finding risk-reward opportunities to short-sell securities. For both problems, we offer novel clustering-based solutions and proceed to show that the clustering criterion addressed in the present research is an excellent choice. We conclude this work by arguing that the financial applications just described exhibit the phenomenon of volatility clustering, which should be more properly targeted by a variance-aware criterion such as the one addressed in this work.
Subject (authority = RUETD)
Topic
Computer Science
Subject (authority = ETD-LCSH)
Topic
Cluster analysis
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_7149
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (xvi, 93 p. : ill.)
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by Rodrigo Franco Toso
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/T33X88TF
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
Toso
GivenName
Rodrigo
MiddleName
Franco
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2016-04-09 23:31:33
AssociatedEntity
Name
Rodrigo Toso
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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Technical

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ETD
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windows xp
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1.5
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DateCreated (point = end); (encoding = w3cdtf); (qualifier = exact)
2016-04-09T23:22:01
DateCreated (point = end); (encoding = w3cdtf); (qualifier = exact)
2016-04-09T23:22:01
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