NDLI: Evolutionary multi-objective clustering for overlapping clusters detection

Content Provider	IEEE Xplore Digital Library
Author	Ripon, K.S.N. Siddique, M.N.H.
Copyright Year	2009
Description	Author affiliation: School of Computing and Intelligent Systems, University of Ulster, United Kingdom (Siddique, M.N.H.) \|\| Computer Science and Engineering Discipline, Khulna University, Bangladesh (Ripon, K.S.N.)
Abstract	Evolutionary algorithms have a history of being applied into clustering analysis. However, most of the existing evolutionary clustering techniques fail to detect complex/spiral-shaped clusters. In our previous works, we proposed several evolutionary multi-objective clustering algorithms and achieved promising results. Still, they suffer from this usual problem exhibited by evolutionary and unsupervised clustering approaches. In this paper, we proposed an improved multi-objective evolutionary clustering approach (EMCOC) to resolve the overlapping problems in complex shape data. Experimental results based on several artificial and real-world data show that the proposed EMCOC can successfully identify overlapping clusters. It also succeeds obtaining non-dominated and near-optimal clustering solutions in terms of different cluster quality measures and classification performance. The superiority of the EMCOC over some other multi-objective evolutionary clustering algorithms is also confirmed by the experimental results.
Starting Page	976
Ending Page	982
File Size	395900
Page Count	7
File Format	PDF
ISBN	9781424429585
DOI	10.1109/CEC.2009.4983051
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-05-18
Publisher Place	Norway
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Evolutionary computation History Clustering algorithms Genetic algorithms Computer science Informatics Intelligent systems Algorithm design and analysis Shape Humans Evolutionary clustering Multi-objective optimization Jumping gene Genetic Algorithm
Content Type	Text
Resource Type	Article

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