A robust fuzzy \(k\)-means clustering model for interval valued data
type: Item
instance of: scholarly article
title: A robust fuzzy \(k\)-means clustering model for interval valued data
zbMATH Open document ID: 1113.62076
author: Pierpaolo D'Urso
author: Paolo Giordani
published in: Computational Statistics
publication date: 2007-05-29T00:00:00Z
review text: The authors propose a \(k\)-means clustering algorithm for multivariate interval-valued data. It generates fuzzy clusters which are represented by the membership degree matrix \(\{u_{iq}\}\), where \(u_{iq}\) indicates the membership degree of the \(i\)-th observation to the \(q\)-th cluster (\(q=1,\dots,k\)). The noise approach is used to reduce the influence of the outliers on the whole partition, i.e., the outliers are assigned to a special cluster of data. The degree of membership to this noise cluster is characterized by \(u_{i*}=1-\sum_{q=1}^k u_{iq}\). Results of simulations and application to a ``Fat and Oils'' data set are presented.
Mathematics Subject Classification ID: 62H30
Mathematics Subject Classification ID: 03E72
zbMATH DE Number: 5158599
zbMATH Keywords: fuzzy cluster
zbMATH Keywords: outlier
zbMATH Keywords: noise cluster
MaRDI profile type: Publication
full work available at URL: s00180-006-0262-y
OpenAlex ID: W1981449145
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DOI: 10.1007/S00180-006-0262-Y
description: scientific article; zbMATH DE number 5158599