R. J. Bayardo Jr. and R. Agrawal. Mining the most interesting rules. In Proceedings of 5th International Conference on Knowledge Discovery and Data Mining, pages 145--154, New York, NY, 1999. ACM Press. Home/Search Document Not in Database Summary Related Articles Check |
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Rules with Bounded Negations and the Coverage Inference Scheme - Balcazar (Correct)
....intended meaning is that transactions containing X tend to contain Y too. Most (though not all) sources also require X and Y to be disjoint. We follow the standard support confidence setting since optimal rules for other interest measures can be found on the optimal support confidence border [BA]; besides, the original formulation of the Coverage Inference Scheme [CS] belonged there. The support of a rule X Y is the support of the itemset X [ Y (or its scaling when all supports are considered scaled by m) the confidence of 3 the rule is sup(X [ Y ) sup(X) The data mining process is ....
R. Bayardo, R. Agrawal. Mining the most interesting rules. In: Proc. of the 5th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining, 145--154, 1999.
On the Complexity of Inducing Categorical and.. - Angiulli, Ianni.. (2003) (Correct)
....association rule describes knowledge valid in the database at hand. For instance, a confidence value of 0.7, associated to the rule above, tells that 70 percent of purchases including hamburgers and fries also include a soft drink. In the literature, several indices have been proposed (see e.g. [7], where several quality criteria are proposed) Clear enough, information patterns expressed in the form of association rules and associated indices indeed represent knowledge that might be useful in several application areas, such as, market basket analysis and fraud detection, just to mention a ....
.... allowed on boolean databases, since conditions on # values are forbidden (and a condition like A c is equivalent to A = #) We analyze the computational complexity of inducing association rules by the most frequently used rule quality indices, namely, confidence, support, # gain and h laplace [7, 2]. Specifically, we shall show that, depending on the chosen index of reference, the complexity of the problem is either in P or NP complete. When databases with nulls are considered, independently of the reference index, the rule induction task is NP complete. However, we show that there are cases ....
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Roberto J. Bayardo, Jr and R. Agrawal. Mining the most interesting rules. Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 15-18, 1999.
Discovering Associations With Numeric Variables - Webb (2001) (2 citations) (Correct)
....large numbers of associations. This imposes a large burden on the data analyst who must determine manually which of these associations are of interest. An active area of research is the identi cation of suitable measures of interestingness that might be applied to automatically lter associations [6, 11, 12]. This is also of importance for impact rule discovery. Aumann and Lindell [3] suggested that distribution measures be used to measure interestingness for impact rules. Their examples include the deviation from that of the training set as a whole of the mean or variance of the target for the ....
R. J. Bayardo and R. Agrawal. Mining the most interesting rules. In KDD-99, pages 145-154, 1999.
Mining Association Rules with Uncertain Item.. - Shyu, Haruechaiyasak.. (Correct)
....a list of items that customers purchase in a single transaction. Many extensions to the original association rule mining [2] have been proposed to deal with some of the original algorithm s drawbacks. Examples include the use of the interestingness measure to prune down the number of rules [3][11] mining the generalized association rules involving hierarchical data set [17] and generalized affinity based association rule mining [15] 16] Most of these extensions including the original algorithm make an assumption that the data set under consideration is precise or consistent and ....
R. J. Bayardo Jr. and R. Agrawal, "Mining the Most Interesting Rules," In S. Chaudhuri and D. Madigan, editors, Proc. of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM Press, pp. 145-154, 1999.
A Framework for Incremental Generation of Frequent Closed.. - Valtchev, Missaoui, Godin (2002) (Correct)
....methods, formal concept analysis, Galois lattices. 1 Introduction Association rule mining within a transaction database [2] is a classical data mining topic, whereby the most challenging problem is the detection of frequently occurring patterns in the transaction sets (frequent itemsets) [1, 4, 11]. A major difficulty with association rules is the prohibitive number of frequent itemsets (and hence association rules) that results from even a reasonably large dataset. The frequent closed itemsets (FCIs) research topic [21, 13, 14] constitutes a promising approach to the problem of reducing ....
R.J. Bayardo and R. Agrawal. Mining the most interesting rules. In Proceedings of the 5th International Conference on Knowledge Discovery and Data Mining (KDD'99), 1999.
CrystalClear: Active Visualization of Association Rules - Ong, Ong, Ng, Lim (Correct)
....in too many rules been produced from a run of the data mining algorithm. While increasing the support and confidence level reduces the rule count, the consequence is the loss of important rules that have low support in the database [16] To overcome this, additional measures has been proposed [9, 15] to prune away rules that has been objectively identified as having no contributions to insights. Effectively, this helps the analyst focus on rules that might be useful. However, this approach has its own pitfalls. First, the use of additional measures at best achieves a reduction on the number ....
R. J. Bayardo and R. Agrawal. Mining the Most Interesting Rules. In Proc. of the 5th Int. Conf. on Knowledge Discovery and Data Mining, pages 145--154, San Diego, CA, USA, Aug. 1999.
Algorithms for Clustering High Dimensional and - Tao (Correct)
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R. J. Bayardo Jr. and R. Agrawal. Mining the most interesting rules. In Proceedings of 5th International Conference on Knowledge Discovery and Data Mining, pages 145--154, New York, NY, 1999. ACM Press.
Association-Based Similarity Testing and Its Applications - Tao Li Department (Correct)
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R. J. Bayardo Jr. and R. Agrawal. Mining the most interesting rules. In Proceedings of 5th International Conference on Knowledge Discovery and Data Mining, pages 145--154, New York, NY, 1999. ACM Press.
March 2002 - Un Vers Ty (Correct)
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R. J. Bayardo Jr. and R. Agrawal. Mining the most interesting rules. In Proceedings of 5th International Conference on Knowledge Discovery and Data Mining, pages 145--154, New York, NY, 1999. ACM Press.
Generating Non-Redundant Association Rules - Mohammed Zaki Computer (2000) (47 citations) (Correct)
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R. J. Bayardo and R. Agrawal. Mining the most interesting rules. In 5th ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, August 1999.
BLOSOM: A Framework for Mining Arbitrary - Boolean Expressions Over (Correct)
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R. J. Bayardo and R. Agrawal. Mining the most interesting rules. In ACM SIGKDD Conf, 1999.
Using Information-Theoretic Measures to Assess.. - Blanchard, Guillet, .. (2005) (Correct)
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R. J. Bayardo and R. Agrawal. Mining the most interesting rules. In Proceedings of ACM KDD'1999.
Assessing Rule Interestingness with a Probabilistic.. - Blanchard, Guillet, .. (2005) (Correct)
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Roberto J. Bayardo and Rakesh Agrawal. Mining the most interesting rules. In Proceedings of the fifth ACM SIGKDD international conference on knowledge discovery and data mining, pages 145--154. ACM Press, 1999.
Using Association Rules to Make Rule-based Classifiers Robust - Hu, Li (2005) (Correct)
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Bayardo, R. & Agrawal, R. (1999), Mining the most interesting rules, in `Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining', ACM Press, N.Y., pp. 145--154.
A Hybrid Approach for Mining Maximal Hyperclique Patterns - Yaochun Huang Univ (2004) (Correct)
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R. Bayardo and R. Agrawal. Mining the Most Interesting Rules. In Proc. of the ACM SIGKDD Conference, 1999.
Interestingness of Frequent Itemsets Using Bayesian.. - Jaroszewicz, Simovici (2004) (Correct)
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R. J. Bayardo and R. Agrawal. Mining the most interesting rules. In Proc. of the 5th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining, pages 145--154, August 1999.
Generating Non-Redundant Association Rules - Zaki (2000) (47 citations) (Correct)
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R. J. Bayardo and R. Agrawal. Mining the most interesting rules. In 5th ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, August 1999.
Information-Theoretical and Combinatorical Methods in Data-Mining - Jaroszewicz (2003) (Correct)
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R. J. Bayardo and R. Agrawal. "Mining the Most Interesting Rules." In Proc. of the 5th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining, pp. 145--154, August 1999.
Generating Non-Redundant Association Rules - Mohammed Zaki Computer (2000) (47 citations) (Correct)
No context found.
R. J. Bayardo and R. Agrawal. Mining the most interesting rules. In 5th ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, August 1999.
Interestingness of Frequent Itemsets Using Bayesian.. - Jaroszewicz, Simovici (Correct)
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R. J. Bayardo and R. Agrawal. Mining the most interesting rules. In Proc. of the 5th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining, pages 145--154, August 1999.
MIRAGE: A Framework for Mining, Exploring and Visualizing.. - Zaki, Phoophakdee (Correct)
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R. J. Bayardo and R. Agrawal. Mining the most interesting rules. In 5th ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, August 1999.
Mining Unexpected Rules by Pushing User Dynamics - Wang, Jiang, Lakshmanan (2003) (Correct)
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R. Bayardo and R. Agrawal. Mining the most interesting rules. In SIGKDD. SIGKDD, 1999.
Mining the Smallest Association Rule Set for Predictions - Jiuyong Li Hong (2001) (1 citation) (Correct)
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R. Bayardo and R. Agrawal. Mining the most interesting rules. In S. Chaudhuri and D. Madigan, editors, Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 145--154, N.Y., Aug. 15--18 1999. ACM Press.
Similarity Testing Between Heterogeneous Basket Datasets - Li, al. (2002) (Correct)
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R. J. Bayardo Jr. and R. Agrawal. Mining the most interesting rules. In Proceedings of 5th International Conference on Knowledge Discovery and Data Mining, pages 145--154, New York, NY, 1999. ACM Press.
Association Rule Mining on Remotely Sensed Imagery Using P-Trees - Ding (2002) (3 citations) (Correct)
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R. J. Bayardo, Jr. and R. Agrawal, "Mining the Most Interesting Rules," Proceedings of the KDD, San Diego, CA, August 1999, pp. 145-154.
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