A robust fuzzy k-means clustering model for interval valued data

被引：0
|
作者：
Pierpaolo D’Urso
Paolo Giordani
机构：
[1] Università degli Studi del Molise,Dipartimento di Scienze Economiche, Gestionali e Sociali
[2] Università di Roma “La Sapienza”,Dipartimento di Statistica Probabilità e Statistiche Applicate
来源：
Computational Statistics | 2006年 / 21卷
关键词：
Fuzzy ; -means; Robust clustering; Interval valued data; Noise center; Noise radius; Noise cluster;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In this paper a robust fuzzy k-means clustering model for interval valued data is introduced. The peculiarity of the proposed model is the capability to manage anomalous interval valued data by reducing the effects of such outliers in the clustering model. In the interval case, the concept of anomalous data involves both the center and the width (the radius) of an interval. In order to show how our model works the results of a simulation experiment and an application to real interval valued data are discussed.
引用
收藏
页码：251 / 269
页数：18
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