Maximum Weighted Entropy Clustering Algorithm
Li Lao, Xiao-Ming Wu, Lingpeng Cheng, Xuefeng Zhu · 2006
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