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.
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