Brief bibliography of interestingness measure, bayesian belief network and causal inference papers
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This document provides a brief bibliography of papers related to interestingness measures, Bayesian belief networks, and causal inference. It lists over 60 references published between 1961-2012 on topics such as outlier detection, probabilistic graphical models, sensitivity analysis of Bayesian networks, rule interestingness measures, and applications of Bayesian networks in domains like fraud detection and credit risk evaluation. The references are grouped into sections on learning Bayesian networks from data, sensitivity analysis, outlier detection, rule interestingness measures, and applications.
Brief bibliography of interestingness measure, bayesian belief network and causal inference papers
- 1. A Brief Bibliography of Interestingness Measure, Bayesian Belief Network and Causal Inference Papers by Adnan Masood Doctoral Student http://scis.nova.edu/~adnan Graduate School of Computer and Information Sciences Nova Southeastern University 2012
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- 4. 4 Hilderman, R., & Hamilton, H. (2001). Knowledge discovery and measures of interest. Springer Netherlands. Hornik, K., Leisch, F., & Zeileis, A. (2003, March). Learning bayesian networks with r. In K. Hornik, F. Leisch, & A. Zeileis (Eds.), Proceedings of the 3rd international workshop on distributed statistical computing (dsc 2003) (p. 2). Vienna, Austria. Available from http://www.r-project.org/conferences/DSC-2003/Proceedings/BottcherDethlefsen.pdf Howard, R., & Matheson, J. (2005). Influence diagrams. Decision Analysis, 2(3), 127–143. Huang, C., Chen, M., & Wang, C. (2007). Credit scoring with a data mining approach based on support vector machines. Expert Systems with Applications, 33(4), 847–856. Huelsenbeck, J., & Ronquist, F. (2001). Mrbayes: Bayesian inference of phylogenetic trees. Bioinformatics, 17(8), 754-755. Imberman, S., Domanski, B., & Thompson, H. (2001). Boolean analyzer-an algorithm that uses a probabilis- tic interestingness measure to find dependency/association rules in a head trauma data. Jaroszewicz, S., & Scheffer, T. (2005). Fast discovery of unexpected patterns in data, relative to a bayesian network. In Proceedings of the eleventh acm sigkdd international conference on knowledge discovery in data mining (pp. 118–127). Jaroszewicz, S., Scheffer, T., & Simovici, D. (2009). Scalable pattern mining with bayesian networks as background knowledge. Data Mining and Knowledge Discovery, 18(1), 56–100. Jaroszewicz, S., & Simovici, D. (2004). Interestingness of frequent itemsets using bayesian networks as background knowledge. In Proceedings of the tenth acm sigkdd international conference on knowledge discovery and data mining (pp. 178–186). Jensen, F. (1997). Bayesian networks and influence diagrams. Risk Management Strategies in Agriculture, 199–213. Joshi, M. V., Agarwal, R. C., & Kumar, V. (2001). Mining needle in a haystack: classifying rare classes via two-phase rule induction. In Proceedings of the 2001 acm sigmod international con- ference on management of data (pp. 91–102). New York, NY, USA: ACM. Available from http://doi.acm.org/10.1145/375663.375673 Kaur, H. (2005). Actionable rules: issues and new directions. In Proceedings of world academy of science, engineering and technology (pp. 61–64). World Academy of Science, Engineering and Technology. Khairuddin, M., Zhang, P., & Rao, A. (2008). Risk mitigation strategies for the prepaid card issuer in aus- tralia. Security Research Centre, School of Computer and Security Science, Edith Cowan University, Perth, Western Australia. Khashman, A. (2010, September). Neural networks for credit risk evaluation: Investigation of differ- ent neural models and learning schemes. Expert Syst. Appl., 37(9), 6233–6239. Available from http://dx.doi.org/10.1016/j.eswa.2010.02.101 Kirkos, E., Spathis, C., & Manolopoulos, Y. (2007). Data mining techniques for the detection of fraudulent financial statements. Expert Systems with Applications, 32(4), 995-1003. Knorr, E. M., & Ng, R. T. (1998). Algorithms for mining distance-based outliers in large datasets. In Proceedings of the 24rd international conference on very large data bases (pp. 392–403). San Francisco, CA, USA: Morgan Kaufmann Publishers Inc. Available from http://dl.acm.org/citation.cfm?id=645924.671334 Koller, D., & Friedman, N. (2009). Probabilistic graphical models. MIT press. Korb, K. B., & Nicholson, A. E. (2010). Bayesian artificial intelligence, second edition (2nd ed.). Boca Raton, FL, USA: CRC Press, Inc. Koski, T., & Noble, J. (2009). Bayesian networks: An introduction (1st ed.). Wiley Publishing. Krieg, M. (2001). A tutorial on bayesian belief networks. Lacave, C., & Díez, F. (2002). A review of explanation methods for bayesian networks. The Knowledge Engineering Review, 17(2), 107–127. Lallich, S., Vaillant, B., & Lenca, P. (2005). Parametrised measures for the evaluation of as- sociation rule interestingness. In Proceedings of the 6th international symposium on ap-
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