A simplified support vector clustering algorithm
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.