智能系统学报2016，Vol.11Issue(4)：561-566,6.DOI:10.11992.tis.201507026
一种基于模板缩减的新型粒子群遗传聚类算法
A novel PSO-GGA for clustering based on pattern reduction
摘要
Abstract
To address the flaws in clustering speed, this paper proposes a novel PSO⁃GGA clustering algorithm based on pattern reduction. To fully combine the pattern reduction method, the algorithm uses a generalized genetic algorithm in serial to improve the particle swarm optimization algorithm. This can increase the diversity of samples and protect patterns that need to be saved for compression. At the same time, to determine the number of particles needed to replace the poor particles an incremental strategy is employed. This fully embodies the PSO’ s ability for rapid search optimization and the genetic algorithm’ s advantage of a large search space. The experimental results show that the clustering time only required 20 percent compared to the original algorithm without showing any obvi⁃ous decline in accuracy.关键词
模板缩减/粒子群/广义遗传算法/聚类Key words
pattern reduction/PSO/generalized genetic algorithm/clustering分类
信息技术与安全科学引用本文复制引用
贾旋,周治平..一种基于模板缩减的新型粒子群遗传聚类算法[J].智能系统学报,2016,11(4):561-566,6.基金项目
江苏省自然科学基金项目（ BK20131107）；江苏省产学研联合创新资金－前瞻性联合研究项目（ BY2013015-33）． （）
