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Comparative Study of Fuzzy PSO (FPSO) Clustering Algorithm and Fuzzy C-Means (FCM) Clustering Algorithm

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dc.contributor.author Oo, Phyo Phyo
dc.contributor.author Htoon, Ei Chaw
dc.date.accessioned 2019-10-15T16:12:49Z
dc.date.available 2019-10-15T16:12:49Z
dc.date.issued 2019-03
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/2297
dc.description.abstract Swarm intelligence that mimic the natural collective intelligence to solve the computational problem has emerged and widely used in data mining. Particle swarm optimization (PSO) is a kind of swarm intelligence algorithm. Fuzzy clustering is an important research in several real-world applications. Fuzzy particle swarm optimization (FPSO) is a fuzzy clustering algorithm that can be optimized with the use of PSO algorithm to get global optima. Fuzzy c-means (FCM) is one of the most popular fuzzy clustering techniques. In this paper, FPSO and FCM clustering algorithms will be implemented. These two methods were compared in term of execution times and fuzzy objective function ( �� ) by using datasets namely Iris Plants, Breast Cancer and Wine from UCI (University of California) Machine Learning repository. en_US
dc.language.iso en_US en_US
dc.publisher National Journal of Parallel and Soft Computing en_US
dc.relation.ispartofseries Vol-1, Issue-1;
dc.subject Fuzzy Clustering en_US
dc.subject Particle Swarm Optimization en_US
dc.subject Fuzzy Particle Swarm Optimization (FPSO) en_US
dc.subject Fuzzy C-means (FCM) en_US
dc.title Comparative Study of Fuzzy PSO (FPSO) Clustering Algorithm and Fuzzy C-Means (FCM) Clustering Algorithm en_US
dc.type Article en_US


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