QPSO 优化的改进 CLARANS 聚类算法

摘要

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 solu-tion. 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.%　　CLARANS 算法是一种有效且广泛应用的聚类算法，适合发现任意形状的聚类结果，但 CLARANS 算法在搜索过程中容易陷入局部最优解，从而忽略全局最优解。为了避免 CLARANS 算法在搜索中心点时易受局部最优解的影响，提出一种将 CLARANS 算法中的邻接点作为 QPSO 算法的量子粒子，结点代价作为适应度函数对其进行寻优的改进 CLARANS算法。将该改进算法应用于 UCI 数据集，结果表明该算法聚类效果好、收敛快，算法的稳定性、收敛性及寻优能力都有很大提高。