Multi-objective multi-view clustering ensemble based on evolutionary approach

Clustering ensembles is a clustering technique which derives a better clustering solution from a set of candidate clustering solutions. Clustering ensemble methods have to address two distinct but interlinked problems: Generating multiple candidate solutions from the data and producing a final clustering solution. Our recently proposed clustering ensembles method (MMOEA) based on NSGA-II used multiple views to address the first problem and a novel cluster oriented approach to address the second problem. MMOEA used a simple crossover method to explore the search space and three objective functions to determine the quality of a candidate clustering solution. The use of a simple crossover method led to slow convergence and using three objectives in NSGA-II framework is often discouraged. This paper presents a new clustering ensemble method, which introduces new ideas for crossover, mutation, tuning steps and two objective functions (instead of three) in an evolutionary process. The results show that our new method outperforms recent methods for clustering ensembles on different multi-view datasets.