基于人工蜂群的三支k-means聚类算法  

Three-way k-means Clustering Based on Artificial Bee Colony

作　　者：徐天杰 王平心 杨习贝[1] XU Tianjie;WANG Pingxin;YANG Xibei(School of Computer,Jiangsu University of Science and Technology,Zhenjiang,Jiangsu 212003,China;School of Science,Jiangsu University of Science and Technology,Zhenjiang,Jiangsu 212003,China)

机构地区：[1]江苏科技大学计算机学院,江苏镇江212003 [2]江苏科技大学理学院,江苏镇江212003

出　　处：《计算机科学》2023年第6期116-121,共6页Computer Science

基　　金：国家自然科学基金(62076111,61773012);江苏省高校自然科学基金(15KJB110004)。

摘　　要：聚类在数据挖掘技术中起着至关重要的作用。传统的聚类算法都是硬聚类算法,即对象要么属于一个类,要么不属于一个类,在处理不确定数据时,强制划分会带来决策错误。三支k-means聚类算法可以对边界不确定数据进行更加合理的分类,但仍然存在对初始聚类中心敏感的问题。为解决这一问题,将人工蜂群算法与三支k-means聚类算法相结合,提出了一种基于人工蜂群的三支k-means聚类算法。通过定义类内聚集度函数和类间离散度函数来构造蜜源的适应度函数,引导蜂群向高质量的蜜源进行全局搜索。利用蜂群之间不同角色的相互协作与互换,对数据集进行多次迭代聚类,找到最优的蜜源位置,作为初始聚类中心,并在此基础上交替迭代聚类。实验证明,该方法对聚类结果的性能指标有所提高。在UCI数据集上的实验验证了该算法的有效性。Clustering plays an important role in data mining technology.Traditional clustering algorithms are hard clustering algorithms,namely,objects either belong to a class or do not belong to a class.However,when dealing with uncertain data,forced division will lead to decision-making errors.Three-way k-means clustering algorithm can divide the data into several groups with uncertain boundary reasonably.But it is still sensitive to the initial clustering center.In order to solve this problem,this paper presents a three-way k-means clustering algorithm based on artificial bee colony by integrating artificial bee colony algorithm with three-way k-means clustering algorithm.The fitness function of honey source is constructed by class cohesion function and inter class dispersion function to guide the bee colony to search for high-quality honey source globally.Using the cooperation and exchange of different roles between bee colonies,the data set is clustered repeatedly to find the optimal honey source location,which is used as the initial clustering center,and on this basis,iterative clustering is carried out alternately.Experiments show that this method improves the performance index of clustering results.The effectiveness of the algorithm is verified on UCI data set.

关 键 词：三支k-means聚类算法 人工蜂群算法 适应度函数 初始聚类中心 蜜源 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]