Publication:
Clustering GSM subscribes for telecom churn management

dc.contributor.authorKarahoca, Adem
dc.contributor.authorKara, Ali
dc.contributor.institutionKarahoca, Adem, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey
dc.contributor.institutionKara, Ali, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey
dc.date.accessioned2025-10-05T16:51:01Z
dc.date.issued2006
dc.description.abstractMobile telecommunication sector has been accelerated with GSM 1800 licenses in the Turkey. Since then, churn management has won vital importance for the GSM operators. Customers should have segmented according to their profitability for the chum management. If we know the profitable customer segments, we have chance to keep in hand the most important customers via the suitable promotions and campaigns. In this study, we implemented clustering algorithms to 250 subscribers' 100MBs call detail records(CDRs), demographic data and billing information. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) compared with K-Means, Expectation Maximization and Farthest-First clustering techniques. As a result, DBSCAN has good distinct clusters for profiling customer segments. © 2008 Elsevier B.V., All rights reserved.
dc.identifier.endpage1600
dc.identifier.issn22243402
dc.identifier.issn17900832
dc.identifier.issue8
dc.identifier.scopus2-s2.0-33745571574
dc.identifier.startpage1595
dc.identifier.urihttps://hdl.handle.net/20.500.14719/13987
dc.identifier.volume3
dc.language.isoen
dc.relation.sourceWSEAS Transactions on Information Science and Applications
dc.subject.authorkeywordsBenchmarking Of Clustering Methods
dc.subject.authorkeywordsChurn Management
dc.subject.authorkeywordsClustering Algorithms
dc.subject.authorkeywordsClustering For Gsm
dc.subject.authorkeywordsData Mining
dc.subject.authorkeywordsDbscan
dc.subject.authorkeywordsAlgorithms
dc.subject.authorkeywordsBenchmarking
dc.subject.authorkeywordsData Mining
dc.subject.authorkeywordsTelecommunication Services
dc.subject.authorkeywordsCall Detail Records (cdrs)
dc.subject.authorkeywordsChurn Management
dc.subject.authorkeywordsClustering Algorithms
dc.subject.authorkeywordsGlobal System For Mobile Communications
dc.subject.indexkeywordsAlgorithms
dc.subject.indexkeywordsBenchmarking
dc.subject.indexkeywordsData mining
dc.subject.indexkeywordsTelecommunication services
dc.subject.indexkeywordsCall detail records (CDRs)
dc.subject.indexkeywordsChurn management
dc.subject.indexkeywordsClustering algorithms
dc.subject.indexkeywordsGlobal system for mobile communications
dc.titleClustering GSM subscribes for telecom churn management
dc.typeArticle
dcterms.referencesKarahoca, Adem, Data mining via cellular neural networks in the GSM sector, Proceedings of the Eigtht IASTED International Conference on Software Engineering and Applications, pp. 19-24, (2004), Proc Kdd, (1996), Beckmann, Norbert, The R-tree: An Efficient and Robust Access Method for Points and Rectangles, SIGMOD Record, 19, 2, pp. 322-331, (1990), Stonebraker, Michael R., The SEQUOIA 2000 storage benchmark, SIGMOD Record, 22, 2, pp. 2-11, (1993)
dspace.entity.typePublication
local.indexed.atScopus
person.identifier.scopus-author-id57197710803
person.identifier.scopus-author-id14026887600

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