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Multi-Objective Complete Fuzzy Clustering Approach



The process of data clustering has mainly focused on optimizing a single objective function, and thus, some information is not used for clustering. Therefore, the aim of this study is to propose a multi-objective complete fuzzy clustering model (MoCFC) that simultaneously optimizes data compactness, separation, and connectedness. The model employs two optimization algorithms; AUGMECON and NSGA-II. Using some fuzzy datasets, the results show that AUGMECON has lower convergence and coverage than NSGA-II, but a higher success index. Moreover, in terms of various cluster validity indices, AUGMECON achieves better performance. However, NSGA-II is the better choice if execution time is critical.



Total Pages: 10
Pages: 285-294


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Volume: 23
Issue: 2
Year: 2016

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ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)

SJR: "The two years line is equivalent to journal impact factor ™ (Thomson Reuters) metric."

Journal: 1995-Present


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