计算机科学 ›› 2024, Vol. 51 ›› Issue (12): 166-173.doi: 10.11896/jsjkx.240600002
薛任煊1, 伊士超2, 王平心2
XUE Renxuan1, YI Shichao2, WANG Pingxin2
摘要： 聚类用于将数据集中的对象划分为具有相似特征的组或类别,使得同一组内的对象之间的相似度较高,而不同组之间的相似度较低。密度聚类是无监督聚类方法之一,它不需要提前指定类簇的数量,而是根据数据的密度来自动确定。与K均值等方法相比,密度聚类对初始点的选择不敏感,因此更容易得到稳健的聚类结果。在众多的密度聚类算法中,DENCLUE(DENsity-based CLUstEring)算法采取了爬山策略,它具有坚实的数学基础,在大量噪声的数据集中具有良好的聚类性能,且在高维数据集中允许对任意形状进行聚类。但其在处理大规模数据集时,需要耗费大量的计算资源和时间。为此,使用粒计算的粒化模型来构建数据集。首先构建一个粗粒度的粒球,然后将粗粒度的粒球划分为细粒球,最后以粒球的形式作为DENCLUE算法的输入,从而进行聚类。实验结果表明,该算法在多个数据集上具有有效性。
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