计算机工程与应用2019，Vol.55Issue(18)：140-145,6.DOI:10.3778/j.issn.1002-8331.1904-0280
基于改进粒子群算法的k均值聚类算法
K-Means Cluster Algorithm Based on Improved PSO
摘要
Abstract
The k-means cluster algorithm based on Improved PSO(IPK-means)is proposed for k-means algorithm’s disadvantage that it is vulnerable to the influence of the initial center, adding chaotic search process to the Particle Swarm Optimization(PSO)algorithm in order to increase the PSO iteration late particle swarm diversity, and in the process of particle update, it proposes a dynamic adjustment factor formula, which makes the adjustment factor related to the fitness value of the particle size, different particles in the same iteration also have different adjustment factors. Finally, the improved PSO algorithm is applied to k -means clustering to find a better initial center for it. The experimental results show that this algorithm can achieve better clustering results.关键词
粒子群优化/k均值聚类/混沌搜索/自适应调整因子Key words
particle swarm optimization/k-means cluster/chaotic searching/adaptive adjustment factor分类
信息技术与安全科学引用本文复制引用
汤深伟,贾瑞玉..基于改进粒子群算法的k均值聚类算法[J].计算机工程与应用,2019,55(18):140-145,6.基金项目
国家科技支撑计划(No.2015BAK24B01) （No.2015BAK24B01）
徽文化传播智能交互技术集成与应用示范项目. （）
