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Comparative Study of Fuzzy PSO (FPSO) Clustering Algorithm and Fuzzy C-Means (FCM) Clustering Algorithm
http://hdl.handle.net/20.500.12678/0000004358
http://hdl.handle.net/20.500.12678/000000435871e526c5-78ce-45a3-b93e-cc77e469cb7a
d106be05-937e-4297-aff6-d2f4f2cb5fcf
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NJPSC 2019 Proceedings-pages-62-67.pdf (624 Kb)
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Article | ||||||
Upload type | ||||||
Publication | ||||||
Title | ||||||
Title | Comparative Study of Fuzzy PSO (FPSO) Clustering Algorithm and Fuzzy C-Means (FCM) Clustering Algorithm | |||||
Language | en_US | |||||
Publication date | 2019-03 | |||||
Authors | ||||||
Oo, Phyo Phyo | ||||||
Htoon, Ei Chaw | ||||||
Description | ||||||
Swarm intelligence that mimic the naturalcollective intelligence to solve the computationalproblem has emerged and widely used in data mining.Particle swarm optimization (PSO) is a kind of swarmintelligence algorithm. Fuzzy clustering is animportant research in several real-world applications.Fuzzy particle swarm optimization (FPSO) is a fuzzyclustering algorithm that can be optimized with the useof PSO algorithm to get global optima. Fuzzy c-means(FCM) is one of the most popular fuzzy clusteringtechniques. In this paper, FPSO and FCM clusteringalgorithms will be implemented. These two methodswere compared in term of execution times and fuzzyobjective function ( �� ) by using datasets namely IrisPlants, Breast Cancer and Wine from UCI (Universityof California) Machine Learning repository. | ||||||
Keywords | ||||||
Fuzzy Clustering, Particle Swarm Optimization, Fuzzy Particle Swarm Optimization (FPSO), Fuzzy C-means (FCM) | ||||||
Identifier | http://onlineresource.ucsy.edu.mm/handle/123456789/2297 | |||||
Journal articles | ||||||
National Journal of Parallel and Soft Computing | ||||||
Conference papers | ||||||
Books/reports/chapters | ||||||
Thesis/dissertations |