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Cluster ensembles
Author(s) -
Ghosh Joydeep,
Acharya Ayan
Publication year - 2011
Publication title -
wiley interdisciplinary reviews: data mining and knowledge discovery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.506
H-Index - 47
eISSN - 1942-4795
pISSN - 1942-4787
DOI - 10.1002/widm.32
Subject(s) - cluster analysis , computer science , variety (cybernetics) , consensus clustering , cluster (spacecraft) , data mining , reuse , set (abstract data type) , data science , machine learning , artificial intelligence , correlation clustering , cure data clustering algorithm , engineering , programming language , waste management
Cluster ensembles combine multiple clusterings of a set of objects into a single consolidated clustering, often referred to as the consensus solution. Consensus clustering can be used to generate more robust and stable clustering results compared to a single clustering approach, perform distributed computing under privacy or sharing constraints, or reuse existing knowledge. This paper describes a variety of algorithms that have been proposed to address the cluster ensemble problem, organizing them in conceptual categories that bring out the common threads and lessons learnt while simultaneously highlighting unique features of individual approaches. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 305–315 DOI: 10.1002/widm.32 This article is categorized under: Technologies > Structure Discovery and Clustering