A method for automatically determining The number of clusters of LAC↑
Han Liu, Qingfeng Wu, Huailin Dong, Shuangshuang Wang, Qing Cai, Zhuo Ma · 2009
The algorithm of locally adaptive clustering for high dimensional data (LAC) processes soft subspace clustering by local weightings of features. To solve the localization of LAC in specifying the number of clusters, this paper reworks the validity index for fuzzy clustering to evaluate the clustering results of LAC. Compared with real clustered data, the method is proved feasible. In the new algorithm, validity function is calculated under different clusters to discover the best clustering number. Experiments have shown that the improved LAC could search for the true number of clusters in high dimensional data sets automatically, as well as elevation of its clustering accuracy.