关键词: 聚类分析, 模糊C-均值（FCM）, 量子粒子群（QPSO）, 新距离标准

Abstract: Fuzzy C-Means（FCM） clustering algorithm has the shortcomings of being sensitive to the initial cluster centers and being trapped by local optima, To resolve two disadvantages, this paper proposes a novel clustering method using Quantum-behavior Particle Swarm Optimization（AQPSO） to optimize the improved FCM clustering algorithm（AF-AQ-AF）, AQPSO algorithm is introduced based on a new metric standard which can lower the influence of initialized data points, quickly converge to the optimal solution and improve the global search ability. Data experimental results show the proposed algorithm avoids entering local minimum, enhances the convergence rate and gets a better result of clustering.

Key words: cluster analysis, Fuzzy C-Means（FCM）, Quantum-behavior Particle Swarm Optimization（QPSO）, new metric standard