Fuzzy clustering with the entropy of attribute weights
Zhou J.1,2; Chen L.2; Chen C.L.P.2; Zhang Y.1; Li H.-X.3
Source PublicationNeurocomputing

For many datasets, it is a difficult work to seek a proper cluster structure which covers the entire feature set. To extract the important features and improve the clustering, the maximum-entropy-regularized weighted fuzzy c-means (EWFCM) algorithm is proposed in this paper. A new objective function is developed in the proposed algorithm to achieve the optimal clustering result by minimizing the dispersion within clusters and maximizing the entropy of attribute weights simultaneously. Then the kernelization of proposed algorithm is realized for clustering the data with 'non-spherical' shaped clusters. Experiments on synthetic and real-world datasets have demonstrated the efficiency and superiority of the presented algorithms. © 2016 Elsevier B.V.

KeywordAttribute-weighted Clustering Feature Selection Kernel Method Maximum-entropy Regularization
URLView the original
Indexed BySCI
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000377230300017
The Source to ArticleScopus
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Cited Times [WOS]:71   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
Affiliation1.Univ Jinan, Sch Informat Sci & Engn, Jinan, Peoples R China
2.Univ Macau, Fac Sci & Technol, Macau, Peoples R China
3.City Univ Hong Kong, Dept Syst Engn & Engn Management, Hong Kong, Hong Kong, Peoples R China
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Zhou J.,Chen L.,Chen C.L.P.,et al. Fuzzy clustering with the entropy of attribute weights[J]. Neurocomputing,2016,198:125.
APA Zhou J.,Chen L.,Chen C.L.P.,Zhang Y.,&Li H.-X..(2016).Fuzzy clustering with the entropy of attribute weights.Neurocomputing,198,125.
MLA Zhou J.,et al."Fuzzy clustering with the entropy of attribute weights".Neurocomputing 198(2016):125.
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