Scaling up inductive learning with massive parallelism
Machine Learning
Activity monitoring: noticing interesting changes in behavior
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining and Knowledge Discovery
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Employing Latent Dirichlet Allocation for fraud detection in telecommunications
Pattern Recognition Letters
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This article describes the development of a prototype data mining system for detecting cellular phone (cloning) fraud. In cellular cloning fraud, the identity of a legitimate cellular phone is programmed into another; from the second phone, calls can be made illicitly that are charged to the customer's account. The system for detecting such fraud is based on a framework that uses a sequence of data mining techniques. First, a rule learning program discovers general indicators of fraudulent behavior from a large database of defrauded accounts. Next, the indicators are used to create a set of monitors, which profile customer behavior and measure anomalies. Finally, the outputs of the monitors are assigned weights by a linear threshold unit. Experiments with the system indicate that this automatic approach performs better than hand-crafted methods for detecting fraud. Furthermore, the system can be retrained as necessary to accommodate changing conditions of fraud detection environments.