关键词: 量子粒子群算法, 基于随机选择的聚类算法（CLARANS）算法, 结点代价, 聚类, 适应度函数

Abstract: CLARANS algorithm is an efficient and effective and wide application clustering algorithm. It is applicable to locate objects with polygon shape. CLARANS often gets stuck at a locally optimum configuration, ignores the global optimum solution. This paper presents an improved CLARANS algorithm based on the QPSO algorithm in order to avoid local optimum. The improved method adopts the quantum particle as the neighbor and takes the node cost as the fitness function. The improved CLARANS algorithm is applied to the UCI data set. The simulation experiment results show that it can improve the clustering performance.

Key words: Quantum Particle Swarm Optimization（QPSO） algorithm, Clustering Algorithm based on Randomized Search（CLARANS） algorithm, node cost, clustering, fitness function