基于粒子群优化的目标分类算法

Target Classification Algorithm Based on Particle Swarm Optimization

定义一个确定聚类数K和初始数据中心的算法,将由算法得到的初始数据中心作为初始粒子,用粒子群优化算法寻优,获得最优数据中心;使用模糊K-Means算法,采用最优数据中心进行聚类.在UCI数据集上的实验结果表明,算法能准确实现分类,具有较强的全局寻优能力和较快的收敛能力,寻优时间较少,能有效地解决目标分类问题.

An algorithm defined for obtaining cluster count K and initial data center is proposed. The initial data center is used as the initial particle, and the particle swarm algorism is used to get the optimum data center; With the fuzzy K-Means algorism, the optimum data center is used for clustering. The experiment on UCI data set shows the method can realize classification accurately with the strong global optimization ability and rapid convergence ability. It has less optimizing time and can solve the goal classification problem effectively.