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基于K近邻相对密度的聚类方法
May 13, 2016, 16:00-17:00
对于聚类分析来说,如何在保证算法易用的条件下发现大规模数据集中不同形态(尺度,形状,密度)的簇是一个重大挑战。经典的聚类算法(比如KMeans,DBSCAN,Density Peak等)都难以兼顾数据的不同形态。我们将探讨一种基于K近邻相对密度的聚类算法。该方法首先通过KNN核方法估计出所有点的密度,根据KNN核密度算出相对密度,利用相对密度我们可以发现不同密度、不同尺度的初始簇(簇纯度较高但可能不完全),最后利用K近邻图上的α可达关系对初始簇合并,得到最终聚类结果。