Tractable reasoning problems with fully-characterized association rules

The support and confidence of association rules are defined in terms of itemset frequencies. While deciding the satisfiability of a set of itemset frequencies is known to be an NPTIME complete problem when frequencies are specified through rational ranges, this complexity result is too wide. To achi...

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Autor principal: Minuto Espil, M.
Formato: Acta de conferencia Capítulo de libro
Lenguaje:Inglés
Publicado: 2012
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100 1 |a Minuto Espil, M. 
245 1 0 |a Tractable reasoning problems with fully-characterized association rules 
260 |c 2012 
270 1 0 |m Minuto Espil, M.; FCEyN, Universidad de Buenos AiresArgentina 
506 |2 openaire  |e Política editorial 
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504 |a Agrawal, R., Srikant, R., Fast Algorithms for Mining Association Rules Proc. of the 20th International Conference on Very Large Databases (1994) 
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504 |a Balcázar, J.L., Minimum-Size Bases of Association Rules (2008) LNCS (LNAI), 5211, pp. 86-101. , Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008, Part I. Springer, Heidelberg 
504 |a Dyer, M.E., Linear Time Algorithms for Two- and Three-Variable Linear Programs (1984) SIAM Journal of Computing, 13 (1), pp. 31-45 
504 |a Calders, T., Computational Complexity of Itemset Frequency Satisfiability (2004) 23th ACM Symposium on Principles of Database Systems PODS Proceedings, pp. 143-154. , ACM 
504 |a Calders, T., Rigotti, C., Boulicaut, J., A Survey on Condensed Representations for Frequent Sets (2006) LNCS (LNAI), 3848, pp. 64-80. , Boulicaut, J.-F., De Raedt, L., Mannila, H. (eds.) Constraint-Based Mining and Inductive Databases. Springer, Heidelberg 
504 |a Casali, A., Cicchetti, R., Lakhal, L., Essential Patterns: A Perfect Cover of Frequent Patterns (2005) LNCS, 3589, pp. 428-437. , Tjoa, A.M., Trujillo, J. (eds.) DaWaK 2005. Springer, Heidelberg 
504 |a Jzefowska, J., Lawrynowicz, A., Lukaszewski, T., The role of semantics in mining frequent patterns from knowledge bases in description logics with rules (2010) TPLP, 10 (3), pp. 251-289 
504 |a Kacprzyk, J., Zadrozny, S., Fuzzy linguistic summaries via association rules (2001) Data Mining and Computational Intelligence, pp. 115-139. , Springer, Physica 
504 |a Kryszkiewicz, M., Gajek, M., Concise Representation of Frequent Patterns Based on Generalized Disjunction-Free Generators (2002) LNCS (LNAI), 2336, pp. 159-171. , Chen, M.-S., Yu, P.S., Liu, B. (eds.) PAKDD 2002. Springer, Heidelberg 
504 |a Kryszkiewicz, M., Rybiński, H., Cichoń, K., On Concise Representations of Frequent Patterns Admitting Negation (2010) SCI, 263, pp. 259-289. , Koronacki, J., Raś, Z.W., Wierzchoń, S.T., Kacprzyk, J. (eds.) Advances in Machine Learning II. Springer, Heidelberg 
504 |a Lenstra Jr., H., Integer Programming with a Fixed Number of Variables (1983) Mathematics of Operations Research, 8 (4), pp. 538-548 
504 |a Luxemburger, M., Implications Partielles dans un Contexte, Mathematique (1991) Informatique et Sciences Humaines, 29 (113), pp. 35-55 
504 |a Minuto Espil, M., RDF Semantics for Web Association Rules (2011) LNCS, 6902, pp. 269-274. , Rudolph, S., Gutierrez, C. (eds.) RR 2011. Springer, Heidelberg 
504 |a Megiddo, N., Linear-Time Algorithms for Linear Programming in R3 and Related Problems (1983) SIAM Journal of Computing, 12 (4), pp. 759-776 
504 |a Megiddo, N., Linear Programming in Linear Time When the Dimension Is Fixed (1984) Journal of ACM, 31 (1), pp. 114-127 
504 |a Morzy, M., Efficient Mining of Dissociation Rules (2006) LNCS, 4081, pp. 228-237. , Tjoa, A.M., Trujillo, J. (eds.) DaWaK 2006. Springer, Heidelberg 
504 |a Ng, R.T., Semantics, Consistency and Query Processing of Empirical Deductive Databases (1993) 10th International Conference on Logic Programming ICLP Proceedings, pp. 812-826. , MIT Press 
504 |a Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L., Efficient Mining of Association Rules Using Closed Itemset Lattices (1999) Information Systems, 24 (1), pp. 25-46 
504 |a Pasquier, N., Taouil, R., Bastide, Y., Stumme, G., Lakhal, L., Generating a Condensed Representation for Association Rules (2005) Journal on Intelligent Information Systems, 24 (1), pp. 29-60 
504 |a Savasere, A., Omiecinski, A., Navathe, S., Mining for Strong Negative Associations in a Large Database of Customer Transactions (1998) 14th International Conference on Data Engineering, ICDE 1998, pp. 494-502. , IEEE CS 
504 |a Srikant, R., Agrawal, R., Mining Generalized Association Rules Proc. of the 21st International Conference on Very Large Databases (1995) 
504 |a Wu, X., Zhang, C., Zhang, C., Efficient mining of both positive and negative association rules (2004) ACM Transaction on Information Systems, 22 (3), pp. 381-405 
504 |a Zaki, M., Mining Non-Redundant Association Rules (2004) Data Mining and Knowledge Discovery, 9 (3), pp. 223-248 
504 |a Zaki, M., Ogihara, M., Theoretical Foundations of Association Rules (1998) 3rd ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, pp. 71-78 
504 |a Zaki, M., Parthasarathy, S., Ogihara, M., Li, W., New Algorithms for Fast Discovery of Association Rules (1997) 3rd International Conference on Knowledge Discovery and Data Mining, pp. 283-286A4 - Allegro Group; City of Poznan; IBM; Roche; Microsoft 
520 3 |a The support and confidence of association rules are defined in terms of itemset frequencies. While deciding the satisfiability of a set of itemset frequencies is known to be an NPTIME complete problem when frequencies are specified through rational ranges, this complexity result is too wide. To achieve tractability, two simpler problems are studied, instead. Both receive a set of association rules as input, each rule provided with exact support and confidence values, and the decision is to be made, respectively on the consistency of the addition and on the implication of a goal rule. Both allow bounds for the support and confidence values of the goal to be specified, and only admit itemsets relevant to the rules to have non-empty extensions in a model. We show that the problems are tractable and efficient algorithms for them are presented. © 2012 Springer-Verlag.  |l eng 
593 |a FCEyN, Universidad de Buenos Aires, Argentina 
690 1 0 |a COMPLETE PROBLEMS 
690 1 0 |a COMPLEXITY RESULTS 
690 1 0 |a ITEM SETS 
690 1 0 |a ITEMSET 
690 1 0 |a SATISFIABILITY 
690 1 0 |a SUPPORT AND CONFIDENCE 
690 1 0 |a TRACTABLE REASONING 
690 1 0 |a ALGORITHMS 
690 1 0 |a INFORMATION SYSTEMS 
690 1 0 |a ASSOCIATION RULES 
711 2 |c Poznan  |d 18 September 2012 through 21 September 2012  |g Código de la conferencia: 93239 
773 0 |d 2012  |g v. 7503 LNCS  |h pp. 282-295  |p Lect. Notes Comput. Sci.  |n Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)  |x 03029743  |w (AR-BaUEN)CENRE-983  |z 9783642330735  |t 16th East European Conference on Advances in Databases and Information Systems, ADBIS 2012 
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856 4 0 |u https://doi.org/10.1007/978-3-642-33074-2_21  |y DOI 
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