Title :
Maximum Weighted Entropy Clustering Algorithm
Author :
Lao, Li ; Wu, Xiaoming ; Cheng, Lingpeng ; Zhu, Xuefeng
Author_Institution :
Coll. of Autom. Sci. & Eng., South China Univ. of Technol., Guangzhou
Abstract :
Combining with the conception of minimum spanning tree in graph theory and with entropy in information theory, a new algorithm is proposed for clustering. An objective function of the weighted entropy based on intra-variance in cluster and variance between clusters is built. The cluster result for the data set is derived from the maximum objective function. This algorithm doesn´t need the prior knowledge about the cluster number and the initialization centre
Keywords :
entropy; pattern clustering; trees (mathematics); graph theory; information theory; intravariance; maximum objective function; maximum weighted entropy clustering algorithm; minimum spanning tree; Algorithm design and analysis; Automation; Clustering algorithms; Clustering methods; Data analysis; Educational institutions; Entropy; Graph theory; Information theory; Tree graphs; Clustering analysis; Graph theory; Objective function; Weighted entropy;
Conference_Titel :
Networking, Sensing and Control, 2006. ICNSC '06. Proceedings of the 2006 IEEE International Conference on
Conference_Location :
Ft. Lauderdale, FL
Print_ISBN :
1-4244-0065-1
DOI :
10.1109/ICNSC.2006.1673291
