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
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;
Conference_Titel :
Information Visualisation, 2008. IV '08. 12th International Conference
Conference_Location :
London
Print_ISBN :
978-0-7695-3268-4