An application of a metaheuristic algorithm-based clustering ensemble method to APP customer segmentation

作者：Kuo R J^*; Mei C H; Zulvia F E; Tsai C Y

来源：Neurocomputing, 2016, 205: 116-129.

DOI：10.1016/j.neucom.2016.04.017

• Cluster analysis
• Clustering ensembles
• Genetic algorithm
• Particle swarm optimization
• Artificial bee colony
• 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.

• 出版日期2016-9-12