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基于蝙蝠算法的K均值聚类算法
引用本文:王晓东,张姣,薛红.基于蝙蝠算法的K均值聚类算法[J].吉林大学学报(信息科学版),2016,34(6):805-810.
作者姓名:王晓东  张姣  薛红
作者单位:西安工程大学理学院,西安,710048;西安工程大学理学院,西安,710048;西安工程大学理学院,西安,710048
基金项目:陕西省自然科学基金资助项目(2016JM1031)
摘    要:为解决传统 K-means 算法中因初始聚类中心选择不当而导致聚类结果陷入局部极值的问题, 采用蝙蝠算法搜寻 K-means 算法的初始聚类中心, 并将模拟退火的思想和基于排挤的小生境技术引入到蝙蝠算法中, 以克服原始蝙蝠算法存在后期收敛速度慢、 搜索力不强等问题。 同时, 通过测试函数验证了其有效性。 最后利用改进后的蝙蝠算法优化 K-means 算法的初始聚类中心, 并将该改进的算法与传统的 K-means 算法的聚类结果进行了对比。 实验结果表明, 改进后的算法的聚类性能比传统的 K-means 算法有很大提高。

关 键 词:K-均值聚类  蝙蝠算法  初始聚类中心
收稿时间:2016-05-08

K-Means Clustering Algorithm Based on Bat Algorithm
WANG Xiaodong,ZHANG Jiao,XUE Hong.K-Means Clustering Algorithm Based on Bat Algorithm[J].Journal of Jilin University:Information Sci Ed,2016,34(6):805-810.
Authors:WANG Xiaodong  ZHANG Jiao  XUE Hong
Abstract:in order to solve the problem of clustering center improper selection in the traditional K-means algorithm which leads to the clustering result into local optimum, the initial clustering center of K-means algorithm is searched by the bat algorithm. The simulated annealing and the niche technology based on crowding out is added into the bat algorithm, in order to overcome some problems such as slow-speed convergence in later and weak search capability, its validity is verified by test functions. Finally the initial clustering center of K-means algorithm is optimized by the improved bat algorithm. The improved algorithm is compared to the traditional K-means algorithm, and the experimental results show that the improved algorithm of clustering performance has improved greater than the traditional K-means algorithm.
Keywords:K-means clustering  bat algorithm  initial clustering center
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