Multidimensional data clustering based on fast kernel density estimation

Author

Xun-Fu Yin

Author_Institution

Sch. of Math. & Stat., Southwest Univ., Chongqing, China

Volume

01

fYear

2013

fDate

14-17 July 2013

Firstpage

311

Lastpage

315

Abstract

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.

Keywords

Gaussian processes; belief networks; estimation theory; independent component analysis; mesh generation; pattern clustering; regression analysis; Delaunay triangulation; SBR-KDE; clustering method; data gaussianization; density based clustering; fast kernel density estimation algorithm; independent component analysis; multidimensional data clustering algorithm; pruning process; sparse Bayesian regression; Abstracts; Analytical models; Clustering algorithms; Computational modeling; Kernel; Standards; Delaunay triangulation; Gaussianization; Independent component analysis; Kernel density estimation; Multidimensional clustering; Sparse bayesian regression;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics (ICMLC), 2013 International Conference on

Conference_Location

Tianjin

Type

conf

DOI

10.1109/ICMLC.2013.6890486

Filename

6890486