基于蝙蝠算法的K均值聚类算法

K-Means Clustering Algorithm Based on Bat Algorithm

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。

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.