Abstract Clustering is a significant data processing technique in data mining field. An improved multi-objective clustering algorithm based on quantum particle swarm optimization is proposed. Firstly, an integer coding strategy is introduced for the unknown class centers. Then, an effective particle swarm optimization strategy is designed based on Canopy strategy to predict the number of class centers. An improved discrete quantum update formula is defined to update the particle position by introducing versus, and and difference operators. Finally, the proposed algorithm is applied to seven real datasets and compared with two typical single-objective clustering algorithms and three multi-objective clustering algorithms. Experimental results demonstrate the effectiveness of the proposed algorithm.
Fund:Supported by National Natural Science Foundation of China(No.61473299), The 13th Batch of "Six Talent Peak" High-Level Talent Project of Jiangsu Province(No.DZXX-053)
About author:: ZHANG Yong, born in 1979, Ph.D., professor. His research interests include group intelligent optimization, pattern recognition and robot odor source localization.) WANG Qing(Corresponding author), born in 1992, master student. His research inte-rests include group intelligent optimization.) XIA Changhong, born in 1992, master student. Her research interests include particle swarm optimization.) SUN Xiaoyan, born in 1978, Ph.D., professor. Her research interests include intelligent optimization calculation and application.) GONG Dunwei, born in 1970, Ph.D., professor. His research interests include inte-lligent optimization and control, search-based software engineering.)
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