Fuzzy support vector clustering

Authors:
En-Hui Zheng;Min Yang;Ping Li;Zhi-Huan Song
Affiliations:
College of Information and Electronic Engineering, Zhejiang Gongshang University, Hang Zhou, P.R. China;Computer School, Hangzhou Dianzi University, Hang Zhou, P.R. China;National Lab. of Industrial Control Technology, Institute of Industrial Process Control, Zhejiang University, Hang Zhou, P.R. China;National Lab. of Industrial Control Technology, Institute of Industrial Process Control, Zhejiang University, Hang Zhou, P.R. China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Year:
2006

Citing 3
Cited 0

The nature of statistical learning theory

The nature of statistical learning theory
Support vector clustering

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Fuzzy support vector machines

IEEE Transactions on Neural Networks

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Abstract

Support vector clustering (SVC) faces the same over-fitting problem as support vector machine (SVM) caused by outliers or noises. Fuzzy support vector clustering (FSVC) algorithm is presented to deal with the problem. The membership model based on k-NN is used to determine the membership value of training samples. The proposed fuzzy support vector clustering algorithm is used to determine the clusters of some benchmark data sets. Experimental results indicate that the proposed algorithm actually reduces the effect of outliers and yields better clustering quality than SVC and traditional centroid-based hierarchical clustering algorithm do.