A Method for Automatically Determining The Number of Clusters of LAC
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.
LAC Validity Indez Automatically determing the number of clusters
Han Liu Qingfeng Wu Huailin Dong Shuangshuang Wang Qing Cai Zhuo Ma
Software School, Xiamen Univ., Xiamen 361005, Fujian, China Department of Information Engineering, Zhejiang Normal University
国际会议
第四届国际计算机新科技与教育学术会议(2009 4th International Conference on Computer Science & Education)
南京
英文
1907-1910
2009-07-25（万方平台首次上网日期，不代表论文的发表时间）