Machine Learning
Knowledge Discovery from Telecommunication Network Alarm Databases
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Knowledge Discovery in Alarm Data Analysis
SOFSEM '96 Proceedings of the 23rd Seminar on Current Trends in Theory and Practice of Informatics: Theory and Practice of Informatics
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As the telecommunications market becomes increasingly competitive, it is essential for operators to target their customers effectively, minimize churn, combat fraud efficiently, and optimize the performance of their networks. KDD technology opens new avenues of opportunity in all of these areas thanks to the wealth of data available for exploration and analysis. In fact, telecommunications companies store vast amounts of data such as customer accounts, call data, equipment records, and fault logs, which represent an invaluable source of information that can be exploited through data mining for a number of purposes. This chapter examines three main application areas of data mining in telecommunications: marketing, fraud detection, and network fault prediction. For each area, the main problems and data mining techniques used are described with the help of examples.