کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
416705 | 681398 | 2006 | 13 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: KNN-kernel density-based clustering for high-dimensional multivariate data KNN-kernel density-based clustering for high-dimensional multivariate data](/preview/png/416705.png)
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.
Journal: Computational Statistics & Data Analysis - Volume 51, Issue 2, 15 November 2006, Pages 513–525