An artificial bee colony approach for clustering
An artificial bee colony approach for clustering
复制标题
一种人工蜂群聚类方法
DOI：
10.1016/j.eswa.2009.11.003
复制
发表时间：
2010-07-01
影响因子：
8.5
通讯作者：
Ning, Jiaxu
中科院分区：
文献类型：
--
作者：
Zhang, Changsheng;Ouyang, Dantong;Ning, Jiaxu
聚类是一种流行的数据分析和数据挖掘技术。提出了一种人工蜂群聚类算法，将N个对象最优地划分为K个聚类。Deb规则用于指导每个候选人的搜索方向。该算法已在多个知名的真实数据集上进行了测试，并与其他流行的启发式聚类算法（如GA、SA、TS、ACO和最近提出的K-NM-PSO算法）进行了比较。计算模拟结果显示，在解决的质量和所需的处理时间非常令人鼓舞的结果。2009爱思唯尔有限公司版权所有。
Clustering is a popular data analysis and data mining technique. In this paper, an artificial bee colony clustering algorithm is presented to optimally partition N objects into K clusters. The Deb's rules are used to direct the search direction of each candidate. This algorithm has been tested on several well-known real datasets and compared with other popular heuristics algorithm in clustering, such as GA, SA, TS, ACO and the recently proposed K-NM-PSO algorithm. The computational simulations reveal very encouraging results in terms of the quality of solution and the processing time required. (C) 2009 Elsevier Ltd. All rights reserved.