چکیده مقاله
Data clustering is an important problem in computer science The objective of data clustering is to partition data objects into some groups such that the data objects in the same group are much similar with each other while data objects in different groups are dissimilar This paper proposes SETO FKM method for data clustering that is a combination of stock exchange trading optimization algorithm SETO algorithm and fuzzy K means FKM The objectiveof SETO is to help the FKM to escape from local optima and converge to global optimum solution Experimental results on seven real world data clustering benchmarks show that the SETO FKM outperformed its counterparts
کلیدواژهها
SETO-FKM optimization fuzzy K-means Data clustering stock exchange trading optimization algorithm fuzzy K means SETO FKM
نویسندگان
Hojjat Emami
Department of Computer Engineering, University of Bonab, Bonab, Iran
شیوه ارجاع
Emami, Hojjat,1400,Data clustering using fuzzy K-means and stock exchange trading optimization algorithm,Fourth International Conference on Soft Computing
ارائهشده در
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