Combined use of association rules mining and clustering methods to find relevant links between binary rare attributes in a large data set
type: Item
instance of: scholarly article
title: Combined use of association rules mining and clustering methods to find relevant links between binary rare attributes in a large data set
zbMATH Open document ID: 1452.62460
author: Marie Plasse
author: Ndeye Niang
author: G. Saporta
author: Alexandre Villeminot
author: Laurent Leblond
published in: Computational Statistics and Data Analysis
publication date: 2009-06-02T00:00:00Z
Mathematics Subject Classification ID: 62H30
Mathematics Subject Classification ID: 62-08
zbMATH DE Number: 5560180
zbMATH Keywords: association rules mining
zbMATH Keywords: variable clustering
zbMATH Keywords: large sparse matrix
zbMATH Keywords: binary attributes
zbMATH Keywords: rule relevancy index
describes a project that uses: SAS/STAT
describes a project that uses: SAS
MaRDI profile type: Publication
full work available at URL: j.csda.2007.02.020
OpenAlex ID: W2122182414
cites work: Q3702302
cites work: Metric and Euclidean properties of dissimilarity coefficients
cites work: Exploratory data analysis leading towards the most interesting simple association rules
cites work: Q3156696
cites work: Clustering of Variables Around Latent Components
DOI: 10.1016/J.CSDA.2007.02.020
description: scientific article; zbMATH DE number 5560180