T. Brijs, K. Vanhoof, and G. Wets. "Reducing redundancy in characteristic rule discovery by using integer programming techniques." Intelligent Data Analysis Journal, 4(3), 2000. Home/Search Document Details and Download
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This paper is cited in the following contexts:
Pruning Redundant Association Rules Using Maximum Entropy.. - Jaroszewicz, Simovici (2002) (3 citations) (Correct)
....to our approach. However, our approach has the advantage of giving a more precise, probabilistic quanti cation of the in uence of subrules on the interestingness of a rule. Another approach to pruning discovered rules is based on selecting a minimal set of rules covering the dataset [TKR 95,BVW00] Again, those methods do not take into consideration probabilistic interactions between rules in the cover. Also, they may prune many interesting rules if they cover instances already covered by other rules. A general study of measures of rule interestingness can be found in [BA99,JS01,HH99] ....
T. Brijs, K. Vanhoof, and G. Wets. Reducing redundancy in characteristic rule discovery by using integer programming techniques. Intelligent Data Analysis Journal, 4(3), 2000.
A Data Mining Framework for Optimal Product.. - Brijs, Goethals.. (2000) (3 citations) (Correct)
.... of statistical properties of the rules, such as support and con dence [2] interest [14] intensity of implication [7] J measure [15] and correlation [12] Other measures are based on the syntactical properties of the rules [11] or they are used to discover the leastredundant set of rules [4]. Second, it was recognized that domain knowledge may also play an important role in determining the interestingness of association rules. Therefore, a number of subjective measures of interestingness have been put forward, such as unexpectedness [13] actionability [1] and rule templates [10] ....
T. Brijs, K. Vanhoof, and G. Wets. Reducing redundancy in characteristic rule discovery by using integer programming techniques. In Intelligent Data Analysis Journal, volume 4:3. Elsevier, 2000. To Appear.
A Data Mining Framework for Optimal Product.. - Brijs, Goethals.. (2000) (3 citations) Self-citation (Brijs Vanhoof Wets) (Correct)
.... of statistical properties of the rules, such as support and confidence [2] interest [14] intensity of implication [7] J measure [15] and correlation [12] Other measures are based on the syntactical properties of the rules [11] or they are used to discover the leastredundant set of rules [4]. Second, it was recognized that domain knowledge may also play an important role in determining the interestingness of association rules. Therefore, a number of subjective measures of interestingness have been put forward, such as unexpectedness [13] actionability [1] and rule templates [10] ....
T. Brijs, K. Vanhoof, and G. Wets. Reducing redundancy in characteristic rule discovery by using integer programming techniques. In Intelligent Data Analysis Journal, volume 4:3. Elsevier, 2000. To Appear.
Information-Theoretical and Combinatorical Methods in Data-Mining - Jaroszewicz (2003) (Correct)
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T. Brijs, K. Vanhoof, and G. Wets. "Reducing redundancy in characteristic rule discovery by using integer programming techniques." Intelligent Data Analysis Journal, 4(3), 2000.
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