A Weighted Cluster Ensemble Algorithm Based on Graph
Xiaoping Fan, Yueshan Xie, Zhifang Liao, Xiaoqing Li, Limin Liu · 2011
Cluster ensemble is an effective method to improve the effect in data clustering, but the results of the existing cluster ensemble algorithms are usually not so good when they process the mixed attributes datas, the main reason is that the results of the algorithms are still dispersed. To solve this problem, this paper presents a new weighted cluster ensemble algorithm based on graph theory. It first clusters the datasets and gets cluster members, and then sets weights to each data object with a proposed ensemble function, and determines the relationship between the data-pair by setting weights to the edges between them, so it can get a weighted nearest neighbor graph. At last it does a last-clustering based on graph theory. Experiments show that the accuracy and stability of this cluster ensemble algorithm is better than other clustering ensemble algorithms.