计算机科学 ›› 2021, Vol. 48 ›› Issue (1): 145-151.doi: 10.11896/jsjkx.200400043
张煜, 陆亿红, 黄德才
ZHANG Yu, LU Yi-hong, HUANG De-cai
摘要: 由于人们对事物认知的局限性和信息的不确定性,在对决策问题进行聚类分析时,传统的模糊聚类不能有效解决实际场景中的决策问题,因此有学者提出了有关犹豫模糊集的聚类算法。现有的层次犹豫模糊K均值聚类算法没有利用数据集本身的信息来确定距离函数的权值,且簇中心的计算复杂度和空间复杂度都是指数级的,不适用于大数据环境。针对上述问题,文中提出了一种基于密度峰值思想的加权犹豫模糊聚类算法(WHFDP),首先给出了犹豫模糊元素集的补齐方法,并结合变异系数理论给出了新的距离函数权重计算公式,然后利用密度峰值选取簇中心,不仅降低了簇中心计算的复杂度,而且提高了对不同规模以及任意形状数据集的适应性,算法的时间复杂度和空间复杂度也降为多项式级,最后采用典型数据集进行仿真实验,证明了所提算法的有效性。
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