离散多目标量子微粒群聚类算法  

Discrete Multi-objective Quantum Particle Swarm Optimization Clustering Algorithm

作　　者：张勇 王庆[1] 夏长红[1] 孙晓燕[1] 巩敦卫[1] 

机构地区：[1]中国矿业大学信息与控制工程学院,徐州221116

出　　处：《模式识别与人工智能》2017年第3期204-213,共10页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.61473299);江苏省第十三批"六大人才高峰"高层次人才项目(No.DZXX-053)资助~~

摘　　要：聚类是数据挖掘中重要的数据处理方法.文中提出改进的离散多目标量子微粒群聚类算法.针对类中心数目未知的情况,引入整数编码策略,基于Canopy策略预测类中心的数目,设计有效的微粒群初始化策略.通过引入与、并和差异算子,定义改进的离散量子微粒更新公式.将文中算法应用于7组真实数据集,并对比2种典型单目标聚类算法和3种多目标聚类算法,验证文中算法性能.Clustering is a significant data processing technique in data mining field. An improved multi-objective clustering algorithm based on quantum particle swarm optimization is proposed. Firstly, an integer coding strategy is introduced for the unknown class centers. Then, an effective particle swarm optimization strategy is designed based on Canopy strategy to predict the number of class centers. An improved discrete quantum update formula is defined to update the particle position by introducing versus, and and difference operators. Finally, the proposed algorithm is applied to seven real datasets and compared with two typical single-objective clustering algorithms and three multi-objective clustering algorithms. Experimental results demonstrate the effectiveness of the proposed algorithm.

关 键 词：聚类 微粒群优化 多目标 离散变量 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]