Self-adaptive Method of Determining Optimal Number of Clusters in Kernel-based Clustering Algorithm
Hu Lai-zhao · Jisuanji gongcheng · 2007
By investigating the inherent pairwise similarities implicitly defined by the kernel function,this paper defines two statistical similarity coefficients,named as within-cluster and between-cluster average similarity coefficient,which can be used to describe the internal and external similarity between the data items,respectively.And then,an efficient validity index for kernel clustering algorithm is proposed,which has distinct physical meanings,less computational complexity and a certain robustness with respect to Gaussian kernel width.In addition,a self-adaptive kernel clustering(SAKC) algorithm based on the proposed validity index is also developed.The benchmark results demonstrate the effectiveness and performance of the new validity index of SAKC algorithm.