Classifier systems and genetic algorithms
Artificial Intelligence
Partitioning sparse matrices with eigenvectors of graphs
SIAM Journal on Matrix Analysis and Applications
Distributed representation of fuzzy rules and its application to pattern classification
Fuzzy Sets and Systems
The nature of statistical learning theory
The nature of statistical learning theory
Silk from a sow's ear: extracting usable structures from the Web
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Trawling the Web for emerging cyber-communities
WWW '99 Proceedings of the eighth international conference on World Wide Web
Focused crawling: a new approach to topic-specific Web resource discovery
WWW '99 Proceedings of the eighth international conference on World Wide Web
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
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A learning system based on genetic adaptive algorithms
A learning system based on genetic adaptive algorithms
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
Email as spectroscopy: automated discovery of community structure within organizations
Communities and technologies
Improving a Pittsburgh Leant Fuzzy Rule Base using Feature Subset Selection
HIS '04 Proceedings of the Fourth International Conference on Hybrid Intelligent Systems
A framework for analysis of dynamic social networks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A Hierarchical Fuzzy Classification of Online Customers
ICEBE '06 Proceedings of the IEEE International Conference on e-Business Engineering
A framework for community identification in dynamic social networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Community Mining from Signed Social Networks
IEEE Transactions on Knowledge and Data Engineering
Sociological factors affecting trust development in virtual communities
International Journal of Networking and Virtual Organisations
The role of social capital in virtual teams and organisations: corporate value creation
International Journal of Networking and Virtual Organisations
How virtuality affects knowledge work: points on performance and knowledge management
International Journal of Networking and Virtual Organisations
Effectiveness of Machine Learning Techniques for Automated Identification of Calling Communities
IV '08 Proceedings of the 2008 12th International Conference Information Visualisation
Calling communities analysis and identification using machine learning techniques
Expert Systems with Applications: An International Journal
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Computing communities in large networks using random walks
ISCIS'05 Proceedings of the 20th international conference on Computer and Information Sciences
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Estimation of urban commuting patterns using cellphone network data
Proceedings of the ACM SIGKDD International Workshop on Urban Computing
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The analysis of logs related to social communities 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 general, identifying calling communities can be used to determine a particular customer's value according to the general pattern of behaviour of the community that the customer belongs to; this helps in creating an effective targeted marketing design, which is significantly important for increasing profitability. In the telecommunications industry, machine-learning techniques have been applied to the Call Detail Record (CDR) for predicting customer behaviour such as churn prediction. In this paper, we pursue the identification of 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. Customer calling patterns are then input to a classification algorithm to generate a classifier model for predicting the calling communities of a customer. We apply two different classification methods: the Support Vector Machine (SVM) algorithm and the 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.