NDLI: SVM Combined with FCM and PSO for Fuzzy Clustering

Content Provider	IEEE Xplore Digital Library
Author	Yifang Yang Guoqiang Chen Yanchun Guo
Copyright Year	2011
Abstract	FCM algorithm is apt to fall into the local optimization, and what fast FCM algorithm can find optimum is greatly depended on the initialization. PSO-based FCM clustering algorithm avoids the local optima, and also is robust to initialization. The fluctuation however has appeared in the new algorithm, and it had been observed that performance of the clustering algorithms deteriorate with more and more overlaps in the data sets. SVM Classifier can handle linear inseparable problems and has the advantages of high accuracy of classification. Motivated by this observation, in this article a new fuzzy clustering technique that SVM Combined with FCM and PSO for Classification Problems has been proposed. Results of numerical experiments on two standard datasets show that the new algorithm is more efficient than the FCM and PSO-based FCM clustering algorithms, it can not only avoids the local optima and is robust to initialization, and also improve accuracy of classification.
Starting Page	1370
Ending Page	1373
File Size	161297
Page Count	4
File Format	PDF
ISBN	9781457720086
DOI	10.1109/CIS.2011.305
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-03
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Algorithm design and analysis Accuracy Fuzzy C-means Clustering algorithms Educational institutions Classification algorithms Support Vector Machine Indexes Clustering Particle Swarm
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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