计算机工程与应用Issue(20)：110-114,5.DOI:10.3778/j.issn.1002-8331.1401-0271
一种改进的基于粒子群的粗糙K-medoids算法
Improved rough K-me doids algorithm based on particle swarm optimization
摘要
Abstract
The K-me doids algorithm has the disadvantage of global search ability and large amount of the iterative calcu-lation, this paper proposes an improved rough K-medoids algorithm based on Particle Swarm Optimization(PSO). By introducing PSO to strengthen its global search ability and calculating the dissimilarity matrix of sample set to simplify coding particle swarm, the rough set theory provides a processing method of dealing with the indeterminacy problem of boundary objects. It uses memorization technique to improve K-medoids iterative calculation, to reduce the complexity of the algorithm. Through testing the Iris, Mushroom data set of UCI, the new algorithm’s accuracy is improved and the time is shortened.关键词
K-me doids算法/粒子群算法/相异度矩阵/粗糙集/记忆技术Key words
K-me doids algorithm/particle swarm optimization/dissimilarity matrix/rough set/memorization分类
信息技术与安全科学引用本文复制引用
杨志,罗可..一种改进的基于粒子群的粗糙K-medoids算法[J].计算机工程与应用,2014,(20):110-114,5.基金项目
国家自然科学基金（No.11171095，No.71371065）；湖南省自然科学衡阳联合基金（No.10JJ8008）；湖南省科技计划项目（No.2013SK3146）。 （）
