计算机工程与应用2013，Vol.49Issue(1)：176-180,5.DOI:10.3778/j.issn.1002-8331.1112-0488
一种改进的混合蛙跳和K均值结合的聚类算法
Clustering algorithm based on Modified Shuffled Frog Leaping Algorithm and K-means
摘要
Abstract
The traditional K-means algorithm is sensitive to initial point and easy to fall into local optimum. In order to overcome these flaws, a novel clustering method based on the Modified Shuffled Frog Leaping Algorithm and K-means is presented. In this approach, a chaotic local search is introduced to improve the quality of the initial individual. Besides, mutation operating is joined to generate new individual. Simultaneously, a new searching strategy is presented to increase the optimization ability, In addition, K-means algorithm is used according to the variation of the frog' s fitness variance. The experimental results show the proposed method improves the clustering performance, and has the advantages in the global search ability and convergence speed.关键词
聚类/混合蛙跳算法/K均值/变异/搜索策略Key words
clustering/ Shuffled Frog Leaping Algorithm(SFLA)/ K-means/ mutation/ searching strategy分类
信息技术与安全科学引用本文复制引用
许方,张桂珠..一种改进的混合蛙跳和K均值结合的聚类算法[J].计算机工程与应用,2013,49(1):176-180,5.基金项目
国家自然科学基金(No.60665001) （No.60665001）
江南大学自主科研计划(No.JUSRP30909). （No.JUSRP30909）
