计算机科学 ›› 2023, Vol. 50 ›› Issue (6): 116-121.doi: 10.11896/jsjkx.220800150
徐天杰1, 王平心2, 杨习贝1
XU Tianjie1, WANG Pingxin2, YANG Xibei1
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。
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