Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management
Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management
Data Warehouses And Olap: Concepts, Architectures And Solutions
Data Warehouses And Olap: Concepts, Architectures And Solutions
Guest Editorial: Nature-inspired distributed computing
Computer Communications
An overview of anomaly detection techniques: Existing solutions and latest technological trends
Computer Networks: The International Journal of Computer and Telecommunications Networking
Survey of Text Mining II: Clustering, Classification, and Retrieval
Survey of Text Mining II: Clustering, Classification, and Retrieval
Local anomaly detection for mobile network monitoring
Information Sciences: an International Journal
Management Information Systems
Management Information Systems
SO_MAD: SensOr Mining for Anomaly Detection in Railway Data
ICDM '09 Proceedings of the 9th Industrial Conference on Advances in Data Mining. Applications and Theoretical Aspects
Theoretically Optimal Distributed Anomaly Detection
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
Intelligent agents for real time data mining in telecommunications networks
AIS-ADM'07 Proceedings of the 2nd international conference on Autonomous intelligent systems: agents and data mining
Data mining methods for anomaly detection of HTTP request exploitations
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
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The tremendous amount of data are generated and collected by telecommunication companies. These data include call detail data which describe the calls traversing the telecommunication networks as well as network and customer data which mainly describe incomes of telecommunication companies. The amount of data is so huge that manual analysis of these data is impossible. The need to automatically handle such large volumes of data has led to the development of special algorithms and technologies such as data mining, intelligent computer agents, knowledge-based expert systems, etc. Telecommunication companies are strongly interested not only in identifying fraudulent phone calls and identifying network faults but also in forecasting the preferred directions of customer calls or the incomes of the companies. The paper presents a communication real anomaly detection framework, which uses data mining technologies using OLAP cube built for telecommunication data.