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 12 → 2008 十月 15
All Science Journal Classification (ASJC) codes