Novel data clustering methods and applications

dc.contributor.advisor Anastasios Matzavinos
dc.contributor.author Liu, Sijia
dc.contributor.department Mathematics
dc.date 2018-08-11T18:03:08.000
dc.date.accessioned 2020-06-30T02:26:51Z
dc.date.available 2020-06-30T02:26:51Z
dc.date.copyright Sat Jan 01 00:00:00 UTC 2011
dc.date.embargo 2013-06-05
dc.date.issued 2011-01-01
dc.description.abstract <p>The need to interpret and extract possible inferences from high-dimensional data sets has led over the past decades to the development of dimensionality reduction and data clustering techniques. Scientific and technological applications of clustering methodologies include among others bioinformatics, biomedical image analysis and biological data mining. Current research in data clustering focuses on identifying and exploiting information on dataset geometry and on developing robust algorithms for noisy datasets. Recent approaches based on spectral graph theory have been devised to efficiently handle dataset geometries exhibiting a manifold structure, and fuzzy clustering methods have been developed that assign cluster membership probabilities to data that cannot be readily assigned to a specific cluster.</p> <p>In this thesis, we develop a family of new data clustering algorithms that combine the strengths of existing spectral approaches to clustering with various desirable properties of fuzzy methods. More precisely, we consider a slate of "random-walk" distances arising in the context of several weighted graphs formed from the data set, which allow to assign "fuzzy" variables to data points which respect in many ways their geometry. The developed methodology groups together data which are in a sense "well-connected", as in spectral clustering, but also assigns to them membership values as in other commonly used fuzzy clustering approaches. This approach is very well suited for image analysis applications and, in particular, we use it to develop a novel facial recognition system that outperforms other well-established methods.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/etd/10206/
dc.identifier.articleid 1164
dc.identifier.contextkey 2736261
dc.identifier.doi https://doi.org/10.31274/etd-180810-285
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath etd/10206
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/24430
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/etd/10206/Liu_iastate_0097E_12184.pdf|||Fri Jan 14 18:16:17 UTC 2022
dc.subject.disciplines Mathematics
dc.subject.keywords face recognition
dc.subject.keywords fuzzy clustering methods
dc.subject.keywords random walks
dc.subject.keywords spectral clustering
dc.title Novel data clustering methods and applications
dc.type article
dc.type.genre dissertation
dspace.entity.type Publication
relation.isOrgUnitOfPublication 82295b2b-0f85-4929-9659-075c93e82c48
thesis.degree.level dissertation
thesis.degree.name Doctor of Philosophy
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