Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Efficient identification of Web communities
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
A fuzzy prediction model for calling communities
International Journal of Networking and Virtual Organisations
Discovering cancer biomarkers: from DNA to communities of genes
International Journal of Networking and Virtual Organisations
Hi-index | 12.05 |
The analysis of social communities related logs has recently received considerable attention for its importance in shedding light on social concerns by identifying different groups, and hence helps in resolving issues like predicting terrorist groups. In the customer analysis domain, identifying calling communities can be used for determining a particular customer's value according to the general pattern behavior of the community that the customer belongs to; this helps the effective targeted marketing design, which is significantly important for increasing profitability. In telecommunication industry, machine learning techniques have been applied to the Call Detail Record (CDR) for predicting customer behavior such as churn prediction. In this paper, we pursue identifying the calling communities and demonstrate how cluster analysis can be used to effectively identify communities using information derived from the 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 different machine learning techniques to build classifier models and compare them in terms of classification accuracy and computational performance. The reported test results demonstrate the applicability and effectiveness of the proposed approach.