A multidimensional and multimembership clustering method for social networks and its application in customer relationship management
type: Item
instance of: scholarly article
title: A multidimensional and multimembership clustering method for social networks and its application in customer relationship management
zbMATH Open document ID: 1296.62135
DOI: 10.1155/2013/323750
author: Cun-Quan Zhang
author: Di Wan
author: Peixin Zhao
author: Xin Zhang
published in: Mathematical Problems in Engineering
publication date: 2014-10-13T00:00:00Z
review text: Summary: Community detection in social networks plays an important role in cluster analysis. Many traditional techniques for one-dimensional problems have been proven inadequate for high-dimensional or mixed type datasets due to the data sparseness and attribute redundancy. In this paper we propose a graph-based clustering method for multidimensional datasets. This novel method has two distinguished features: nonbinary hierarchical tree and the multi-membership clusters. The nonbinary hierarchical tree clearly highlights meaningful clusters, while the multimembership feature may provide more useful service strategies. Experimental results on the customer relationship management confirm the effectiveness of the new method.
Mathematics Subject Classification ID: 62H30
Mathematics Subject Classification ID: 91D30
Mathematics Subject Classification ID: 91C20
Mathematics Subject Classification ID: 68T10
zbMATH DE Number: 6354656
Wikidata QID: Q59023637
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2013/323750
OpenAlex ID: W2075280697
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description: scientific article; zbMATH DE number 6354656