Kenji Yamanishi
- 1.V. Barnett and T. Lewis, Outliers in Statistical Data, John Wiley &: Sons, 1994.Google Scholar
- 2.F. Bonchi, F. Giannotti, G. Mainetto, and D. Pedeschi, A classification-based methodology for planning audit strategies in fraud detection, in Proc. of KDD-99, pp:175-184, 1999. Google ScholarDigital Library
- 3.P. Burge and J. Shaw-Taylor, Detecting cellular fraud using adaptive prototypes, in Proc. o} AI Approaches to Fraud Detection and Risk Management, pp:9-13, 1997.Google Scholar
- 4.P. Chan and S. Stolfo, Toward scalable learning with non-uniform class and cost-distributions: A case study in credit card fraud detection, in Proc. ofg KDD-98, AAAI-Press, pp:164-168 (1998).Google Scholar
- 5.T. Fawcett and F. Provost, Activity monitoring: noticing interesting changes in behavior, in Proc. of KDD-99, pp:53-62, 1999. Google ScholarDigital Library
- 6.I. Grabec, Self-organization of Neurons described by the maximum-entropy principle, Biological Cybernetics vol. 63, pp:403-409, 1990.Google Scholar
- 7.htt p://kddAcs.uci.edu/databases/kddcup99/kddcup99.htmlGoogle Scholar
- 8.E. M. Knorr and R. T. Ng, Algorithms for mining distance-based outliers in large datasets, in Proc. of the 2th VLDB Con}erence, pp:392-403, 1998. Google ScholarDigital Library
- 9.W. Lee, S. J. Stolfo, and K. W. Mok, Mining in a data-flow environment: experience in network intrusion detection, in Proc. of KDD-99, pp:114-124, 1999. Google ScholarDigital Library
- 10.R. M. Neal and G. E. Hinton, A view of the EM algorithm that justifies incremental, sparse, and other variants, ftp:// ftp.cs.toronto.edu/pub/radford/www/publications.html 1993.Google Scholar
- 11.S. Rosset, U. Murad, E. Neumann, Y. Idan, and G. Pinkas, Discovery of fraud rules for telecommunications-chalenges and solutions, in Proc. of KDD-99, pp:409-413, 1999. Google ScholarDigital Library
Index Terms
- On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms
Recommendations
On-Line Unsupervised Outlier Detection Using Finite Mixtures with Discounting Learning Algorithms
Outlier detection is a fundamental issue in data mining, specifically in fraud detection, network intrusion detection, network monitoring, etc. SmartSifter is an outlier detection engine addressing this problem from the viewpoint of statistical learning ...
Fully Unsupervised Learning of Gaussian Mixtures for Anomaly Detection in Hyperspectral Imagery
ISDA '09: Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and ApplicationsThis paper proposes a fully unsupervised anomaly detection strategy in hyperspectral imagery based on mixture learning. Anomaly detection is conducted by adopting a Gaussian Mixture Model (GMM) to describe the statistics of the background in ...
Visual Speech Detection Using an Unsupervised Learning Framework
ICMLA '13: Proceedings of the 2013 12th International Conference on Machine Learning and Applications - Volume 02This paper presents an unsupervised learning framework for visual speech detection. Bimodal GMM is used to model visual features, i.e., mouth region intensity, which varies during speech. Variation in the mouth region intensity is used for visual speech ...
Comments