An application of a metaheuristic algorithm based clustering ensemble method to APP customer segmentation
This study proposes a metaheuristic based clustering ensemble method. It integrates the clustering ensembles algorithm with the metaheuristic based clustering algorithm. In the clustering ensembles, this study performs an improved generation mechanism and a co association matrix in the co occurrence approach. In order to improve the efficiency, a principle component analysis is employed. Furthermore, three metaheuristic based clustering algorithms are proposed. This paper uses a real coded genetic algorithm, a particle swarm optimization and an artificial bee colony optimization to combine with clustering ensembles algorithm. The experimental results indicate that the proposed metaheuristic based clustering ensembles algorithms have better performance than metaheuristic based clustering without clustering ensembles method. Furthermore, the proposed algorithms are applied to solve a customer segmentation problem. The real problem is come from a mobile application. Among all of the proposed algorithms, the artificial bee colony optimization based clustering ensembles algorithm outperforms other algorithms. Therefore, the marketing strategy for the real application is made based on the best result.
Neurocomputing