Publication: Clustering GSM subscribes for telecom churn management
| dc.contributor.author | Karahoca, Adem | |
| dc.contributor.author | Kara, Ali | |
| dc.contributor.institution | Karahoca, Adem, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey | |
| dc.contributor.institution | Kara, Ali, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey | |
| dc.date.accessioned | 2025-10-05T16:51:01Z | |
| dc.date.issued | 2006 | |
| dc.description.abstract | Mobile 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.endpage | 1600 | |
| dc.identifier.issn | 22243402 | |
| dc.identifier.issn | 17900832 | |
| dc.identifier.issue | 8 | |
| dc.identifier.scopus | 2-s2.0-33745571574 | |
| dc.identifier.startpage | 1595 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14719/13987 | |
| dc.identifier.volume | 3 | |
| dc.language.iso | en | |
| dc.relation.source | WSEAS Transactions on Information Science and Applications | |
| dc.subject.authorkeywords | Benchmarking Of Clustering Methods | |
| dc.subject.authorkeywords | Churn Management | |
| dc.subject.authorkeywords | Clustering Algorithms | |
| dc.subject.authorkeywords | Clustering For Gsm | |
| dc.subject.authorkeywords | Data Mining | |
| dc.subject.authorkeywords | Dbscan | |
| dc.subject.authorkeywords | Algorithms | |
| dc.subject.authorkeywords | Benchmarking | |
| dc.subject.authorkeywords | Data Mining | |
| dc.subject.authorkeywords | Telecommunication Services | |
| dc.subject.authorkeywords | Call Detail Records (cdrs) | |
| dc.subject.authorkeywords | Churn Management | |
| dc.subject.authorkeywords | Clustering Algorithms | |
| dc.subject.authorkeywords | Global System For Mobile Communications | |
| dc.subject.indexkeywords | Algorithms | |
| dc.subject.indexkeywords | Benchmarking | |
| dc.subject.indexkeywords | Data mining | |
| dc.subject.indexkeywords | Telecommunication services | |
| dc.subject.indexkeywords | Call detail records (CDRs) | |
| dc.subject.indexkeywords | Churn management | |
| dc.subject.indexkeywords | Clustering algorithms | |
| dc.subject.indexkeywords | Global system for mobile communications | |
| dc.title | Clustering GSM subscribes for telecom churn management | |
| dc.type | Article | |
| dcterms.references | Karahoca, 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.type | Publication | |
| local.indexed.at | Scopus | |
| person.identifier.scopus-author-id | 57197710803 | |
| person.identifier.scopus-author-id | 14026887600 |
