Title :
A method for automatically determining The number of clusters of LAC↑
Author :
Liu, Han ; Wu, Qingfeng ; Dong, Huailin ; Wang, Shuangshuang ; Cai, Qing ; Ma, Zhuo
Author_Institution :
Software Sch., Xiamen Univ., Xiamen, China
Abstract :
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.
Keywords :
fuzzy set theory; pattern clustering; LAC; fuzzy clustering; high dimensional data; locally adaptive clustering algorithm; soft subspace clustering; validity index; Algorithm design and analysis; Clustering algorithms; Clustering methods; Computer science; Computer science education; Gene expression; Los Angeles Council; Particle measurements; Automatically determing the number of clusters; LAC; Validity Index;
Conference_Titel :
Computer Science & Education, 2009. ICCSE '09. 4th International Conference on
Conference_Location :
Nanning
Print_ISBN :
978-1-4244-3520-3
Electronic_ISBN :
978-1-4244-3521-0
DOI :
10.1109/ICCSE.2009.5228241