Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/75623
Title: Generalized competitive agglomeration clustering algorithm
Authors: Huang, CQ
Chung, FL 
Wang, ST 
Keywords: Competitive agglomeration
Fuzziness index
Entropy index
Fuzzy clustering
Hierarchical clustering
Partitional clustering
Issue Date: 2017
Publisher: Springer
Source: International journal of machine learning and cybernetics, 2017, v. 8, no. 6, p. 1945-1969 How to cite?
Journal: International journal of machine learning and cybernetics 
Abstract: In this paper, a generalized competitive agglomeration (CA) clustering algorithm called entropy index constraints competitive agglomeration (EICCA) is proposed to avoid the drawback that the fuzziness index m in the CA must be fixed to be 2. The proposed EICCA is inspired by a basic fuzzy clustering algorithm called entropy index constraints fuzzy C-means (EIC-FCM), which is comparable to fuzzy C-means (FCM) in clustering performance but completely different from the FCM in the use of entropy index constraints with very clear physical meaning instead of the original constraints in the FCM. With the help of the EIC-FCM, the generalized competitive agglomeration algorithm EICCA is developed by introducing a competition term into the EIC-FCM's objective function, which is similar to the CA by introducing a competition term into the FCM's objective function. Our theoretical analysis and empirical results indicate that the EICCA can effectively find the optimal number of clusters for a dataset to be clustered, with more flexible index choices than the CA having the fuzziness index m = 2 only.
URI: http://hdl.handle.net/10397/75623
ISSN: 1868-8071
EISSN: 1868-808X
DOI: 10.1007/s13042-016-0572-5
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