MULTIDIMENSIONAL DATA CLUSTERING BASED ON FAST KERNEL DENSITY ESTIMATION

被引：0
|
作者：
Yin, Xun-Fu [1 ]
机构：
[1] Southwest Univ, Sch Math & Stat, Chongqing 400715, Peoples R China
来源：
PROCEEDINGS OF 2013 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS (ICMLC), VOLS 1-4 | 2013年
关键词：
Multidimensional clustering; Kernel density estimation; Sparse bayesian regression; Independent component analysis; Delaunay triangulation; Gaussianization; ALGORITHM;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Density based clustering is an important clustering method. This paper presents a novel multidimensional data clustering algorithm based on SBR-KDE, which is a fast kernel density estimation algorithm we developed using sparse Bayesian regression, independent component analysis and data gaussianization. A pruning process of the Delaunay triangulation is also exploited in the clustering algorithm. Experimental studies using practical data and artificial data show the effectiveness of our clustering algorithm.
引用
收藏
页码：311 / 315
页数：5
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