A Multidimensional and Multimembership Clustering Method for Social Networks and Its Application in Customer Relationship Management

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-b...

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Main Authors:	Peixin Zhao, Cun-Quan Zhang, Di Wan, Xin Zhang
Format:	Article
Language:	English
Published:	Hindawi Limited 2013-01-01
Series:	Mathematical Problems in Engineering
Online Access:	http://dx.doi.org/10.1155/2013/323750

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spelling	doaj-418248fc5ac04e6cac82cf569f4458112020-11-24T21:57:25ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472013-01-01201310.1155/2013/323750323750A Multidimensional and Multimembership Clustering Method for Social Networks and Its Application in Customer Relationship ManagementPeixin Zhao0Cun-Quan Zhang1Di Wan2Xin Zhang3School of Management, Shandong University, Jinan, Shandong 250100, ChinaDepartment of Mathematics, West Virginia University, Morgantown, WV 26506, USADepartment of Physics and Astronomy, University of Victoria, Victoria, BC, V8W 2Y2, CanadaFoundation Department, Shandong College of Electronic Technology, Jinan, Shandong 250200, ChinaCommunity 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.http://dx.doi.org/10.1155/2013/323750
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Peixin Zhao Cun-Quan Zhang Di Wan Xin Zhang
spellingShingle	Peixin Zhao Cun-Quan Zhang Di Wan Xin Zhang A Multidimensional and Multimembership Clustering Method for Social Networks and Its Application in Customer Relationship Management Mathematical Problems in Engineering
author_facet	Peixin Zhao Cun-Quan Zhang Di Wan Xin Zhang
author_sort	Peixin Zhao
title	A Multidimensional and Multimembership Clustering Method for Social Networks and Its Application in Customer Relationship Management
title_short	A Multidimensional and Multimembership Clustering Method for Social Networks and Its Application in Customer Relationship Management
title_full	A Multidimensional and Multimembership Clustering Method for Social Networks and Its Application in Customer Relationship Management
title_fullStr	A Multidimensional and Multimembership Clustering Method for Social Networks and Its Application in Customer Relationship Management
title_full_unstemmed	A Multidimensional and Multimembership Clustering Method for Social Networks and Its Application in Customer Relationship Management
title_sort	multidimensional and multimembership clustering method for social networks and its application in customer relationship management
publisher	Hindawi Limited
series	Mathematical Problems in Engineering
issn	1024-123X 1563-5147
publishDate	2013-01-01
description	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.
url	http://dx.doi.org/10.1155/2013/323750
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A Multidimensional and Multimembership Clustering Method for Social Networks and Its Application in Customer Relationship Management

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