KNN-kernel density-based clustering for high-dimensional multivariate data

Authors:
Thanh N. Tran;Ron Wehrens;Lutgarde M. C. Buydens
Affiliations:
Analytical Chemistry, Institute for Molecules and Materials, Radboud University Nijmegen, Nijmegen, The Netherlands;Analytical Chemistry, Institute for Molecules and Materials, Radboud University Nijmegen, Nijmegen, The Netherlands;Analytical Chemistry, Institute for Molecules and Materials, Radboud University Nijmegen, Nijmegen, The Netherlands
Venue:
Computational Statistics & Data Analysis
Year:
2006

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Abstract

Density-based clustering algorithms for multivariate data often have difficulties with high-dimensional data and clusters of very different densities. A new density-based clustering algorithm, called KNNCLUST, is presented in this paper that is able to tackle these situations. It is based on the combination of nonparametric k-nearest-neighbor (KNN) and kernel (KNN-kernel) density estimation. The KNN-kernel density estimation technique makes it possible to model clusters of different densities in high-dimensional data sets. Moreover, the number of clusters is identified automatically by the algorithm. KNNCLUST is tested using simulated data and applied to a multispectral compact airborne spectrographic imager (CASI)_image of a floodplain in the Netherlands to illustrate the characteristics of the method.