Abstract
Pattern discovery is one of the fundamental tasks in data mining. Pattern discovery typically explores a massive space of potential patterns to identify those that satisfy some user-specified criteria. This process entails a huge risk (in many cases a near certainty) that many patterns will be false discoveries. These are patterns that satisfy the specified criteria with respect to the sample data but do not satisfy those criteria with respect to the population from which those data are drawn. This talk discusses the problem of false discoveries, and presents techniques for avoiding them.
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Webb, G.I.: Discovering significant rules. In: Ungar, L., et al. (eds.) Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD-2006, pp. 434–443. ACM Press, New York (2006)
Webb, G.I.: Discovering significant patterns. Machine Learning (in-press)
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© 2007 Springer Berlin Heidelberg
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Webb, G. (2007). Finding the Real Patterns. In: Zhou, ZH., Li, H., Yang, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2007. Lecture Notes in Computer Science(), vol 4426. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71701-0_2
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DOI: https://doi.org/10.1007/978-3-540-71701-0_2
Publisher Name: Springer, Berlin, Heidelberg
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