Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Credit Card Fraud Detection Using Hidden Markov Model
IEEE Transactions on Dependable and Secure Computing
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
Markov monitoring with unknown states
IEEE Journal on Selected Areas in Communications
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Fraud detection is a fundamental data mining task with a wide range of practical applications. Finding rare and evolving fraudulent claimant behaviour in millions of electronic Medicare records poses unique challenges due to the unsupervised nature of the problem. In this paper, we investigate the problem of efficiently and effectively identifying potential non-compliant Medicare claimants in Australia. We propose an unsupervised and data-driven fraud detection system called UNISIM. It integrates various techniques, such as feature selection, clustering, pattern recognition and outlier detection. By utilising the beneficial properties of these techniques, we are able to automate the detection process. Additionally, useful temporal patterns are extracted from the existing data for future analysis. Through extensive empirical studies, UNISIM is shown to effectively identify suspects with highly irregular patterns. Additionally, it is capable of detecting groups of outliers.