C4.5: programs for machine learning
C4.5: programs for 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
Anomaly detection algorithms in logs of process aware systems
Proceedings of the 2008 ACM symposium on Applied computing
ACM Computing Surveys (CSUR)
Fraud detection in process aware systems
Companion Proceedings of the XIV Brazilian Symposium on Multimedia and the Web
In-depth behavior understanding and use: The behavior informatics approach
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Anomaly detection in categorical datasets using bayesian networks
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part II
An effective early fraud detection method for online auctions
Electronic Commerce Research and Applications
Inside insider trading: patterns & discoveries from a large scale exploratory analysis
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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"Inside information" comes in many forms: knowledge of a corporate takeover, a terrorist attack, unexpectedly poor earnings, the FDA's acceptance of a new drug, etc. Anyone who knows some piece of soon-to-break news possesses inside information. Historically, insider trading has been detected after the news is public, but this is often too late: fraud has been perpetrated, innocent investors have been disadvantaged, or terrorist acts have been carried out. This paper explores early detection of insider trading - detection before the news breaks. Data mining holds great promise for this emerging application, but the problem also poses significant challenges. We present the specific problem of insider trading in option markets, compare decision tree, logistic regression, and neural net results to results from an expert model, and discuss insights that knowledge discovery techniques shed upon this problem.