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Mining the Most Interesting Rules (1999)

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by Roberto J. Bayardo
Citations:110 - 1 self
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BibTeX

@INPROCEEDINGS{Bayardo99miningthe,
    author = {Roberto J. Bayardo},
    title = {Mining the Most Interesting Rules},
    booktitle = {},
    year = {1999},
    pages = {145--154}
}

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Abstract

Several algorithms have been proposed for finding the “best, ” “optimal,” or “most interesting ” rule(s) in a database according to a variety of metrics including confidence, support, gain, chi-squared value, gini, entropy gain, laplace, lift, and conviction. In this paper, we show that the best rule according to any of these metrics must reside along a support/confidence border. Further, in the case of conjunctive rule mining within categorical data, the number of rules along this border is conveniently small, and can be mined efficiently from a variety of real-world data-sets. We also show how this concept can be generalized to mine all rules that are best according to any of these criteria with respect to an arbitrary subset of the population of interest. We argue that by returning a broader set of rules than previous algorithms, our techniques allow for improved insight into the data and support more user-interaction in the optimized rule-mining process. 1.

Citations

412 Dynamic itemset counting and implication rules for market basket data - Brin, Motwani, et al. - 1997
409 Fast discovery of association rules - Agrawal, Mannila, et al. - 1996
325 Jr., “Efficiently Mining Long Patterns from Databases - Bayardo - 1998
291 Rule induction with cn2: Some recent improvements - Clark, Boswell - 1991
123 Constraint-based rule mining in large, dense databases - Bayardo, Agrawal, et al.
109 Data mining using two-dimensional optimized association rules: scheme, algorithms and visualization - Fukuda, Morimoto, et al. - 1996
101 Mining associations between sets of items in massive databases - Agrawal, Imielinski, et al. - 1993
57 Partial Classification Using Association Rules - Ali, Manganaris, et al. - 1997
38 Brute-Force Mining of High-Confidence Classification Rules - Bayardo - 1997
38 Abstract-driven pattern discovery in databases - Dhar, Tuzhilin - 1993
23 On classification and regression - Morishita - 1998
16 Algorithms for mining association rules for binary segmentations of huge categorical databases - Morimoto, Fukuda, et al. - 1998
11 Convexity, Cambridge Tracts - Eggleston - 1958
2 A Priori Versus A Posteriori Filtering of Association Rules - Goethals, Bussche - 1999
1 Mining the Most Interesting Rules. IBM Research Report. Available from: http://www.almaden.ibm.com/cs/quest - Bayardo, Agrawal - 1999
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