RUcore: Rutgers University Community Repository
Novel clustering and classification algorithms for big data
Descriptive
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Title
Novel clustering and classification algorithms for big data
Name
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Ghosh
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Debopriya
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1989
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Ghosh, Debopriya, 1989-
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author
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Katehakis
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Michael
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Michael Katehakis
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Advisory Committee
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chair
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Cabrera
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Javier
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Javier Cabrera
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Advisory Committee
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co-chair
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ADI BEN-ISRAEL
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internal member
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Metaxas
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Dmitri
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Dmitri Metaxas
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internal member
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Papadimitriou
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Spyridon
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Spyridon Papadimitriou
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Advisory Committee
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internal member
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Williams
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Jerome
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Jerome Williams
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internal member
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Lubomirski
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Mariusz
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Mariusz Lubomirski
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Advisory Committee
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Rutgers University
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degree grantor
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Graduate School - Newark
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theses
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2020
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2020-05
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English
Abstract
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This dissertation studies two important problems that arise in the analysis of Big Data: high dimensionality and massive size of pertinent samples. In Chapter 1, we developed three novel algorithms for clustering and classification of Big Data. First, a novel two-way clustering approach that combines model-based and weighted K-means clustering methods. The two-way approach results in smaller subgroups of binary features that are of size p or less, so that the possible number of patterns is small enough to be efficiently handled by traditional clustering algorithms. This approach can also handle weighted reduced data to scale on massive sample sizes. In Chapter 2, we derived a weighted probabilistic distance-based clustering technique adjusted for cluster sizes. Chapter 3 introduces an ensemble method called Enriched Random Forest for high dimensional data (where n << p, n is the number of observations and p are the features). This algorithm can address situations where the dimension is very high but only a very small fraction of these features is truly informative.
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Management
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Rutgers University Electronic Theses and Dissertations
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ETD
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Title
Graduate School - Newark Electronic Theses and Dissertations
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rucore10002600001
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ETD_10754
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doi:10.7282/t3-74gr-vt97
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1 online resource (xi, 94 pages)
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Ph.D.
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Includes bibliographical references
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ETD doctoral
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Rights
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The author owns the copyright to this work.
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Name
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Ghosh
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Debopriya
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Permission or license
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2020-04-15 22:30:57
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Debopriya Ghosh
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Affiliation
Rutgers University. Graduate School - Newark
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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.
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2020-05-31
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2022-05-31
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after May 31st, 2022.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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