A simplified support vector clustering algorithm

Li Ying Wu, Jeen-Shing Wang

研究成果: Conference article同行評審

2 引文 斯高帕斯(Scopus)


This paper presents a simplified support vector Clustering (SVC) algorithm for improving the efficiency of the SVC training procedure. The cluster structure obtained by our proposed approach is controlled by two parameters: the parameter of kernel functions, denoted as q; and the percentage of data used to form the contour. The mechanisms we developed Can efficiently search for suitable parameters without much trial- and-error effort for reaching a satisfactory clustering result. From observations of the behavior of the clustering, we found that 1) the search range of q is related to the densities of the Clusters; 2) the number of boundary vectors has much relevance to the computation time; and 3) the shape of the original dataset affects the size of a reduced dataset. We have based our findings to develop a simplified SVC to identify optimal cluster Configuration with suitable cluster contours. Computer simulations have been conducted on benchmark datasets to demonstrate the effectiveness of our proposed approach.

頁(從 - 到)1259-1264
期刊Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
出版狀態Published - 2008 十二月 1
事件2008 IEEE International Conference on Systems, Man and Cybernetics, SMC 2008 - Singapore, Singapore
持續時間: 2008 十月 122008 十月 15

All Science Journal Classification (ASJC) codes

  • 電氣與電子工程
  • 控制與系統工程
  • 人機介面


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