计算机应用研究2018，Vol.35Issue(2)：466-470,5.DOI:10.3969/j.issn.1001-3695.2018.02.031
基于萤火虫优化的加权K-means算法
Weighted K-means clustering algorithm based on firefly algorithm
摘要
Abstract
The traditional K-means algorithm is sensitive to initial cluster and data noise,etc.To overcome these shortages,this paper used the firefly algorithm (FA) which had power ability of global search and quick convergence rate to optimize the initial clustering centers of traditional K-means algorithm.As the same time,it used a kind of weighted Euclidean distance to reduce the defects produced by noise data and other uncertainties.Thus it proposed a weighted K-means clustering algorithm based on FA,which could improve clustering performance as well as the convergence rate of the algorithm.Finally,this paper conducted clustering experiments and validity test on several groups of UCI data.The results show great efficiency and superiority of the proposed algorithm.关键词
加权K-means/聚类/萤火虫算法Key words
weighted K-means/clustering/firefly algorithm分类
信息技术与安全科学引用本文复制引用
陈小雪,尉永清,任敏,孟媛媛..基于萤火虫优化的加权K-means算法[J].计算机应用研究,2018,35(2):466-470,5.基金项目
国家自然科学基金资助项目(61373148,61502151) （61373148,61502151）
国家教育部人文社科基金资助项目(14YJC860042) （14YJC860042）
山东省自然科学基金资助项目(ZR2014FL010) （ZR2014FL010）
山东省优秀中青年科学家奖励基金资助项目(BS2013DX033) （BS2013DX033）
山东省社会科学规划项目(16CFXJ05) （16CFXJ05）
山东省高等学校科技计划项目(J15LN02,J15LN22) （J15LN02,J15LN22）
山东省高等学校人文社会科学研究项目(J15WB37) （J15WB37）
