DocumentCode
2292262
Title
Effectiveness of Machine Learning Techniques for Automated Identification of Calling Communities
Author
Kianmehr, Keivan ; Alhajj, Reda
Author_Institution
Dept. of Comput. Sci., Univ. of Calgary, Calgary, AB
fYear
2008
fDate
9-11 July 2008
Firstpage
308
Lastpage
313
Abstract
In this paper, we demonstrate how cluster analysis can be used to effectively identify communities using information derived from the Call Detail Record (CDR) data. We use the information extracted from the cluster analysis to identify customer calling patterns. Customers calling patterns are then given to a classification algorithm to generate a classifier model for predicting the calling communities of a customer. We apply two different classification methods: Support vector machine and fuzzy-genetic classifier. The latter method is used for possibly assigning a customer to different classes with different degrees of membership. The reported test results demonstrate the applicability and effectiveness of the proposed approach.
Keywords
customer services; fuzzy set theory; genetic algorithms; marketing data processing; pattern classification; pattern clustering; statistical analysis; support vector machines; telecommunication services; unsupervised learning; CDR data; call detail record; cluster analysis; customer calling pattern identification; fuzzy-genetic classifier; marketing; support vector machine; unsupervised machine learning; Classification algorithms; Clustering algorithms; Data mining; Information analysis; Machine learning; Pattern analysis; Predictive models; Support vector machine classification; Support vector machines; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Visualisation, 2008. IV '08. 12th International Conference
Conference_Location
London
ISSN
1550-6037
Print_ISBN
978-0-7695-3268-4
Type
conf
DOI
10.1109/IV.2008.68
Filename
4577964
Link To Document