A novel clustering method based on SVM

Authors:
Jie Li;Xinbo Gao;Licheng Jiao
Affiliations:
School of Electronic Engineering, Xidian Univ., Xi'an, China;School of Electronic Engineering, Xidian Univ., Xi'an, China;School of Electronic Engineering, Xidian Univ., Xi'an, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Year:
2005

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Abstract

For the problem of cluster analysis, the objective function based algorithms are popular and widely used methods. However, the performance of these algorithms depends upon the priori information about cluster number and cluster prototypes. Moreover, it is only effective for analyzing data set with the same type of cluster prototypes. For this end, this paper presents a novel algorithm based on support vector machine (SVM) for realizing fully unsupervised clustering. The experimental results with various test data sets illustrate the effectiveness of the proposed novel clustering algorithm based on SVM.