A clustering method based on the estimation of the probability density function and on the skeleton by influence zones. Application to image processing

被引：39

作者：

Herbin, M ^{[1
]}

Bonnet, N ^{[1
]}

Vautrot, P ^{[1
]}

机构：

[1] UNIV REIMS,F-51100 REIMS,FRANCE

来源：

PATTERN RECOGNITION LETTERS | 1996年 / 17卷 / 11期

关键词：

clustering; probability density function; skeleton by influence zones;

D O I：

10.1016/0167-8655(96)00085-2

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper investigates a new approach to data clustering. The probability density function (p.d.f.) is estimated by using the Parzen window technique. The p.d.f. thresholding permits the segmentation of the data space by influence zones (SKIZ algorithm). A bottom-up thresholding procedure is iterated to refine the segmentation. As a result, a complete partition of the data space is obtained in parallel to the clustering of the data samples. In addition, an estimation of the intrinsic dimensionality of the data set is provided. This approach of clustering is tested with simulated data and applied to color image data.

引用

页码：1141 / 1150

页数：10

共 21 条

[11] A DYNAMIC APPROACH FOR CLUSTERING DATA [J].