Data clustering using fuzzy K-means and stock exchange trading optimization algorithm
محل انتشار: چهارمین کنفرانس بین المللی محاسبات نرم
سال انتشار: 1400
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 153
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شناسه ملی سند علمی:
CSCG04_057
تاریخ نمایه سازی: 23 اسفند 1400
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
کلیدواژه ها:
Data clustering ، optimization ، stock exchange trading optimization algorithm ، fuzzy K-means ، SETO-FKM
نویسندگان
Hojjat Emami
Department of Computer Engineering, University of Bonab, Bonab, Iran