基于QPSO的数据聚类*

在KMeans聚类、PSO聚类、KMeans和PSO混合聚类（KPSO）的基础上，研究了基于量子行为的微粒群优化算法（QPSO）的数据聚类方法，并提出利用KMeans聚类的结果重新初始化粒子群，结合QPSO的聚类算法，即KQPSO。介绍了如何利用上述算法找到用户指定的聚类个数的聚类中心。聚类过程都是根据数据之间的Euclidean（欧几里得）距离。KMeans算法、PSO算法和QPSO算法的不同在于聚类中心向量的“进化”上。最后使用三个数据集比较了上面提到的五种聚类方法的性能，结果显示基于QPSO

Data Clustering Based on Quantum behaved Particle Swarm Optimization

This paper investigates Quantum-behaved Particle Swarm Optimization(QPSO) algorithm to cluster data based on the K-Means clustering,PSO clustering and KPSO clustering.After that we introduce using K-Means clustering to seed the initial swarm,combing with QPSO to cluster data,namely KQPSO and introduce how these algorithms can be used to find the centroids of a user specified number of clusters.All the process of clustering based on the Euclidean distance among data vectors.The differences between K-Means,PSO,QPSO is the evolution of the cluster-centroids.Finally,we compare the perfor-mance of the five clustering method on three data sets.The experiments result show QPSO clustering superiority.