Liste der Themen:

0. -- Einführung --
1. Betrugserkennung in Empfehlungssystemen
   1. Bamshad Mobasher, Robin Burke, Runa Bhaumik, J.J. Sandvig (2007): Attacks and Remedies in Collabortive Recommendation IEEE Intelligent Systems May/June 2007, pp. 56-63.
   
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   2. [ee] Bamshad Mobasher, Robin Burke, Runa Bhaumik, Chad Williams (2007): Towards Trustworthy Recommender Systems: An Analysis of Attack Models and Algorithm Robustness ACM Transactions on Internet Technology, Vol. 7, No. 2, May 2007.
2. Erkennung von Kreditkartenbetrug mit Bayesschen und Neuronalen Netzen
   1. [ee] Sam Maes, Karl Tuyls, Bram Vanschoenwinkel, Bernard Manderick (2002): Credit Card Fraud Detection Using Bayesian and Neural Networks First International NAISO Congress on Neuro Fuzzy Technologies, Havana, Cuba.
3. Betrugserkennung basierend auf Datenfingerabdrücken
   1. [ee] Corinna Cortes, Daryl Pregibon (2001): Signature-Based Methods for Data Streams Data Mining and Knowledge Discovery, Vol. 5, No. 3, Juli 2001, Pages 167-182.
4. Betrugserkennung in sequentiellen Daten
   1. [ee] Terran Lane, Carla E. Brodley (1999): Temporal Sequence Learning and Data Reduction for Anomaly Detection ACM Transactions on Information and System Security, Vol. 2, No. 3, August 1999, Pages 295-331.
   
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   2. [ee] Thomas G. Dietterich (2002): Machine Learning for Sequential Data: A Review Structural, Syntatic, and Statistic Pattern Recognition, Springer Berlin / Heidelberg, ISBN 978-3-540-44011-6.
5. Fallbasierte Systeme zur Betrugserkennung
   1. [ee] Richard Wheeles, Stuard Aitken (2000): Multiple Algorithms for Fraud Detection Knowledge-Based Systems, Elsevier.
6. Relationale Klassifikation zur Betrugserkennung
   1. [ee] Jennifer Neville, Özgür Simsek, David Jensen, John Komoroske, Kelly Palmer, Henry Goldberg (2005): Using Relational Knowledge Discovery to Prevent Securities Fraud Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, Pages 449 - 458, ISBN 1-59593-135-X .
   
   und
   2. [ee] Jennifer Neville, David Jensen, Lisa Friedland, Michael Hay (2003): Learning Relational Probability Trees Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, Pages 625 - 630, ISBN 1-58113-737-0.
7. Boosting Naive Bayes zur Betrugserkennung
   1. [ee] Stijn Viaene, Richard A. Derrig, Guido Dedene (2004): A Case Study of Applying Boosting Naive Bayes to Claim Fraud Diagnosis IEEE Transactions on Knowledge and Data Engineering, Vol. 16, No. 5, May 2004.
8. Entwicklung interner Kontrollsysteme zur Betrugserkennung
   1. [ee] Mieke Jans, Nadine Lybaert, Koen Vanhoof (2007): Data Mining for Fraud Detection: Toward an Improvement on Internal Control Systems? 30th Annual Congress European Accounting Association (EAA2007).
9. Regression zur Vorhersage von Insolvenzfällen (vor allem für Studentinnen/Studenten mit besonderem Interesse an Statistik)
   1. [ee] Dean P. Foster, Robert A. Stine (2004): Variable Selection in Data Mining: Building a Predictive Model for Bankruptcy Journal of the American Statistical Association.
10. Hybride Systeme (Entscheidungsbäume und SVM)
    1. [ee] Sandhya Peddabachigari, Ajith Abraham, Crina Grosan, Johnson Thomas (2007): Modelling intrusion detection system using hybrid intelligent systems Journal of Network and Computer Applications, Volume 30, Issue 1, January 2007, Pages 114-132.