Custer Fusion Algorithm Based on Majority Voting Mechanism
Sheng Jiang · Journal of Chinese Computer Systems · 2007
Taking the one-pass clustering algorithm as the basic algorithm for grouping data, the issue of clustering ensemble is investigated. Over multiple clusters obtained by random threshold and sequence of data input of the one-pass clustering algorithm, produces a mapping of the clusters into an association matrix between patterns. The final data partition is obtained by voting mechanism over this association matrix. Experimental results of the proposed cluster fusion algorithm on several real and synthetic data sets are compared with clustering results produced by well known clustering algorithms. The experimental results show that the proposed algorithm is effective and practicable.