New model for clustering ensemble based on genetic algorithms
Maoting Gao · Computer Engineering and Applications Journal · 2013
Clustering ensemble algorithms require higher differences among clustering components, which induce higher complexity during the generating phase of clustering components. This paper proposes a new model for Clustering Ensemble based on Genetic Algorithm(CEGA), which does not need to consider the differences between clustering components, but translates clustering into optimization of clustering components by calculating target function, and optimizes the grouping of clustering components by genetic algorithms. CEGA sets the final optimal chromosome to be the result of clustering and its complexity and application are also analyzed. Experimental results demonstrate the effectiveness of the proposed method on several UCI datasets.