基于AQPSO的数据聚类

Data clustering using adaptive quantum-behaved particle swarm optimization

提出了一种新的聚类算法——适应性的基于量子行为的微粒群优化算法的数据聚类(AQPSO)。AQPSO在全局搜索能力和局部搜索能力上优于PSO和QPSO算法,它的适应性方法比较接近于高水平智能群体的社会有机体的学习过程,并且能保证种群不断地进化。聚类过程都是根据数据向量之间的Euclidean(欧几里得的)距离。PSO和QPSO的不同在于聚类中心的进化上。QPSO和AQPSO的不同在于参数的选择上。实验中用到4个数据集比较聚类的效果,结果证明了AQPSO聚类方法优于PSO和QPSO聚类方法。

In this paper we propose a new clustering algorithm-Adaptive Quantum-behaved Particle Swarm Optimization（AQPSO）.The QPSO outperforms PSO and QPSO in global search ability and local search ability,because the adaptive method is more approximate to the learning process of social organism with high-level swarm intelligence and can make the population evolve persistently.All the process of clustering based on the Euclidean distance among data vectors.The difference between PSO and QPSO is the evolution of the cluster-centroids,and the difference between QPSO and AQPSO is the selection of the parameter value.We compare the performance of the three clustering method on four datasets,experiments result show AQPSO clustering superiority.