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K-means-based Consensus Clustering : = Algorithms, Theory and Applications.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	K-means-based Consensus Clustering :/
其他題名:	Algorithms, Theory and Applications.
作者:	Liu, Hongfu.
面頁冊數:	1 online resource (174 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Contained By:	Dissertation Abstracts International79-12B(E).
標題:	Computer engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9780438237490

K-means-based Consensus Clustering : = Algorithms, Theory and Applications.
Liu, Hongfu.

K-means-based Consensus Clustering :Algorithms, Theory and Applications. - 1 online resource (174 pages)

Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.

Thesis (Ph.D.)--Northeastern University, 2018.

Includes bibliographical references

Consensus clustering aims to find a single partition which agrees as much as possible with existing basic partitions, which emerges as a promising solution to find cluster structures from heterogeneous data. It has been widely recognized that consensus clustering is effective to generate robust clustering results, detect bizarre clusters, handle noise, outliers and sample variations, and integrate solutions from multiple distributed sources of data or attributes. Different from the traditional clustering methods, which directly conducts the data matrix, the input of consensus clustering is the set of various diverse basic partitions. Therefore, consensus clustering is a fusion problem in essence, rather than a traditional clustering problem. In this thesis, we aim to solve the challenging consensus clustering by transforming it into other simple problems. Generally speaking, we propose K-means-based Consensus Clustering (KCC), which exactly transforms the consensus clustering problem into a K-means clustering problem with theoretical supports, and provide the sufficient and necessary condition of KCC utility functions. Further, based on co-association matrix we propose spectral ensemble clustering, and solve it with a weighted K-means. By this means, we decrease the time and space complexities from O(n3) and O (n2) to both O( n). Finally, we achieve Infinite Ensemble Clustering with a mature technique named marginalized denoising auto-encoder. Derived from consensus clustering, a partition level constraint is proposed as the new side information for constraint clustering and domain adaptation.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780438237490Subjects--Topical Terms:

569006
Computer engineering.
Index Terms--Genre/Form:

554714
Electronic books.

K-means-based Consensus Clustering : = Algorithms, Theory and Applications.
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504 $a Includes bibliographical references
520 $a Consensus clustering aims to find a single partition which agrees as much as possible with existing basic partitions, which emerges as a promising solution to find cluster structures from heterogeneous data. It has been widely recognized that consensus clustering is effective to generate robust clustering results, detect bizarre clusters, handle noise, outliers and sample variations, and integrate solutions from multiple distributed sources of data or attributes. Different from the traditional clustering methods, which directly conducts the data matrix, the input of consensus clustering is the set of various diverse basic partitions. Therefore, consensus clustering is a fusion problem in essence, rather than a traditional clustering problem. In this thesis, we aim to solve the challenging consensus clustering by transforming it into other simple problems. Generally speaking, we propose K-means-based Consensus Clustering (KCC), which exactly transforms the consensus clustering problem into a K-means clustering problem with theoretical supports, and provide the sufficient and necessary condition of KCC utility functions. Further, based on co-association matrix we propose spectral ensemble clustering, and solve it with a weighted K-means. By this means, we decrease the time and space complexities from O(n3) and O (n2) to both O( n). Finally, we achieve Infinite Ensemble Clustering with a mature technique named marginalized denoising auto-encoder. Derived from consensus clustering, a partition level constraint is proposed as the new side information for constraint clustering and domain adaptation.
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