摘要
针对传统k-means算法中初始聚类中心选取的随意性对于聚类结果影响较大的问题,提出了基于Leader算法的k-means改进算法——Lk-means算法.该算法有效避免了初始聚类中心选取的边缘化和随意性.实验证明,Lk-means算法的聚类结果更加有效合理.
To the problem of the signi ficant effect on the cluster results owing to the randomicity of traditional k-means on determining the initial cluster centers, we propose an improved k-means algorithm, Lk-means, which is based on leader algorithm. The Lk-means algorithm is effective to avoid the marginalization and randomicity emerged in determining the initial cluster centers. Experiment proves that the algorithm makes the cluster results more objective and reasonable.
出处
《山西师范大学学报(自然科学版)》
2014年第4期26-29,共4页
Journal of Shanxi Normal University(Natural Science Edition)
基金
山西省自然科学基金(2008011029-2)