Clustering via nonparametric density estimation
Clustering via nonparametric density estimation
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DOI：
10.1007/s11222-006-9010-y
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发表时间：
2007-03-01
影响因子：
2.2
通讯作者：
Torelli, Nicola
中科院分区：
文献类型：
--
作者：
Azzalini, Adelchi;Torelli, Nicola
尽管Hartigan(1975)已经提出了将子总体识别与具有高密度潜在概率分布的区域联系起来的想法，但为了计算方便，聚类分析方法的实际发展在很大程度上转向了其他方向。目前的计算资源允许我们重新考虑这一公式，并直接开发聚类技术，以便识别密度的局部模式。在给定一组观测值的情况下，构造基础密度函数的非参数估计，并通过适当的处理相关的Delaunay三角剖分来形成高密度点子集。用数值算例说明了该方法的有效性。
Although Hartigan (1975) had already put forward the idea of connecting identification of subpopulations with regions with high density of the underlying probability distribution, the actual development of methods for cluster analysis has largely shifted towards other directions, for computational convenience. Current computational resources allow us to reconsider this formulation and to develop clustering techniques directly in order to identify local modes of the density. Given a set of observations, a nonparametric estimate of the underlying density function is constructed, and subsets of points with high density are formed through suitable manipulation of the associated Delaunay triangulation. The method is illustrated with some numerical examples.