An Improved Fuzzy c-Means Clustering Algorithm Based on Shadowed Sets and PSO

To organize the wide variety of data sets automatically and acquire accurate classification, this paper presents a modified fuzzy c-means algorithm (SP-FCM) based on particle swarm optimization (PSO) and shadowed sets to perform feature clustering. SP-FCM introduces the global search property of PSO...

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Vydáno v:	Computational Intelligence and Neuroscience Ročník 2014; číslo 2014; s. 181 - 190
Hlavní autoři:	Zhang, Jian, Shen, Ling
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Cairo, Egypt Hindawi Limiteds 01.01.2014 Hindawi Publishing Corporation John Wiley & Sons, Inc
Témata:	Algorithms Cluster Analysis Clustering Clusters Datasets as Topic Fuzzy Fuzzy Logic Fuzzy set theory Fuzzy sets Gene Expression Humans Optimization algorithms Pattern Recognition, Automated - methods Product Packaging - statistics & numerical data Searching Swarm intelligence Validity Wine - classification Wine - statistics & numerical data Yeasts - genetics
ISSN:	1687-5265, 1687-5273, 1687-5273
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Abstract	To organize the wide variety of data sets automatically and acquire accurate classification, this paper presents a modified fuzzy c-means algorithm (SP-FCM) based on particle swarm optimization (PSO) and shadowed sets to perform feature clustering. SP-FCM introduces the global search property of PSO to deal with the problem of premature convergence of conventional fuzzy clustering, utilizes vagueness balance property of shadowed sets to handle overlapping among clusters, and models uncertainty in class boundaries. This new method uses Xie-Beni index as cluster validity and automatically finds the optimal cluster number within a specific range with cluster partitions that provide compact and well-separated clusters. Experiments show that the proposed approach significantly improves the clustering effect.
AbstractList	To organize the wide variety of data sets automatically and acquire accurate classification, this paper presents a modified fuzzy c-means algorithm (SP-FCM) based on particle swarm optimization (PSO) and shadowed sets to perform feature clustering. SP-FCM introduces the global search property of PSO to deal with the problem of premature convergence of conventional fuzzy clustering, utilizes vagueness balance property of shadowed sets to handle overlapping among clusters, and models uncertainty in class boundaries. This new method uses Xie-Beni index as cluster validity and automatically finds the optimal cluster number within a specific range with cluster partitions that provide compact and well-separated clusters. Experiments show that the proposed approach significantly improves the clustering effect.To organize the wide variety of data sets automatically and acquire accurate classification, this paper presents a modified fuzzy c-means algorithm (SP-FCM) based on particle swarm optimization (PSO) and shadowed sets to perform feature clustering. SP-FCM introduces the global search property of PSO to deal with the problem of premature convergence of conventional fuzzy clustering, utilizes vagueness balance property of shadowed sets to handle overlapping among clusters, and models uncertainty in class boundaries. This new method uses Xie-Beni index as cluster validity and automatically finds the optimal cluster number within a specific range with cluster partitions that provide compact and well-separated clusters. Experiments show that the proposed approach significantly improves the clustering effect. To organize the wide variety of data sets automatically and acquire accurate classification, this paper presents a modified fuzzy c-means algorithm (SP-FCM) based on particle swarm optimization (PSO) and shadowed sets to perform feature clustering. SP-FCM introduces the global search property of PSO to deal with the problem of premature convergence of conventional fuzzy clustering, utilizes vagueness balance property of shadowed sets to handle overlapping among clusters, and models uncertainty in class boundaries. This new method uses Xie-Beni index as cluster validity and automatically finds the optimal cluster number within a specific range with cluster partitions that provide compact and well-separated clusters. Experiments show that the proposed approach significantly improves the clustering effect. To organize the wide variety of data sets automatically and acquire accurate classification, this paper presents a modified fuzzy c -means algorithm (SP-FCM) based on particle swarm optimization (PSO) and shadowed sets to perform feature clustering. SP-FCM introduces the global search property of PSO to deal with the problem of premature convergence of conventional fuzzy clustering, utilizes vagueness balance property of shadowed sets to handle overlapping among clusters, and models uncertainty in class boundaries. This new method uses Xie-Beni index as cluster validity and automatically finds the optimal cluster number within a specific range with cluster partitions that provide compact and well-separated clusters. Experiments show that the proposed approach significantly improves the clustering effect.
Author	Zhang, Jian Shen, Ling
AuthorAffiliation	2 Precision Medical Device Department, University of Shanghai for Science and Technology, Shanghai 200093, China 1 School of Mechanical Engineering, Tongji University, Shanghai 200092, China
AuthorAffiliation_xml	– name: 2 Precision Medical Device Department, University of Shanghai for Science and Technology, Shanghai 200093, China – name: 1 School of Mechanical Engineering, Tongji University, Shanghai 200092, China
Author_xml	– sequence: 1 fullname: Zhang, Jian – sequence: 2 fullname: Shen, Ling
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CitedBy_id	crossref_primary_10_1016_j_ins_2022_01_018 crossref_primary_10_1007_s12040_019_1152_3 crossref_primary_10_1155_2016_9264690 crossref_primary_10_1016_j_eswa_2019_03_051 crossref_primary_10_1007_s10586_020_03112_3 crossref_primary_10_1016_j_ins_2018_10_051 crossref_primary_10_1155_2015_685404 crossref_primary_10_1155_2017_2427309 crossref_primary_10_32890_jict2020_19_4_1 crossref_primary_10_1007_s00500_023_07821_w crossref_primary_10_1016_j_ijar_2020_02_006 crossref_primary_10_1016_j_scitotenv_2016_09_165 crossref_primary_10_1088_1742_6596_1361_1_012002
Cites_doi	10.1109/TCBB.2007.70272 10.1016/j.patcog.2011.01.014 10.1016/j.eswa.2010.07.112 10.1016/j.patrec.2009.09.011 10.1002/int.20111 10.1109/3477.658584 10.1145/1497577.1497578 10.1016/0031-3203(93)90141-I 10.1109/91.531779 10.3233/FI-2013-829 10.1016/j.patcog.2009.09.029 10.1109/TFUZZ.2010.2052258 10.1109/34.85677 10.1016/j.patcog.2003.06.005 10.1002/widm.1050 10.1109/3477.678624 10.1109/TEVC.2013.2281545 10.1016/j.asoc.2014.04.017 10.1002/int.20323 10.1109/TFUZZ.2009.2034529
ContentType	Journal Article
Copyright	Copyright © 2014 Jian Zhang and Ling Shen. Copyright © 2014 Jian Zhang and Ling Shen. Jian Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Copyright © 2014 J. Zhang and L. Shen. 2014
Copyright_xml	– notice: Copyright © 2014 Jian Zhang and Ling Shen. – notice: Copyright © 2014 Jian Zhang and Ling Shen. Jian Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. – notice: Copyright © 2014 J. Zhang and L. Shen. 2014
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Snippet	To organize the wide variety of data sets automatically and acquire accurate classification, this paper presents a modified fuzzy c-means algorithm (SP-FCM)... To organize the wide variety of data sets automatically and acquire accurate classification, this paper presents a modified fuzzy c -means algorithm (SP-FCM)... To organize the wide variety of data sets automatically and acquire accurate classification, this paper presents a modified fuzzy c -means algorithm (SP-FCM)...
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SubjectTerms	Algorithms Cluster Analysis Clustering Clusters Datasets as Topic Fuzzy Fuzzy Logic Fuzzy set theory Fuzzy sets Gene Expression Humans Optimization algorithms Pattern Recognition, Automated - methods Product Packaging - statistics & numerical data Searching Swarm intelligence Validity Wine - classification Wine - statistics & numerical data Yeasts - genetics
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Title	An Improved Fuzzy c-Means Clustering Algorithm Based on Shadowed Sets and PSO
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Volume	2014
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