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Comparison of Heuristics for Optimization of Association Rules

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper, seven greedy heuristics for construction of association rules are compared from the point of view of the length and coverage of constructed rules. The obtained rules are compared also with optimal ones constructed by dynamic programming algorithms. The average relative difference between length of rules constructed by the best heuristic and minimum length of rules is at most 4%. The same situation is with coverage.
Wydawca
Rocznik
Strony
1--14
Opis fizyczny
Bibliogr. 30 poz., tab.
Twórcy
  • Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia
autor
  • Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia
  • Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia
  • Institute of Computer Science, University of Silesia in Katowice, 39 Będzińska St., 41-200 Sosnowiec, Poland
  • Institute of Computer Science, University of Silesia in Katowice, 39 Będzińska St., 41-200 Sosnowiec, Poland
Bibliografia
  • [1] Agrawal R, Imieliński T, Swami A. Mining Association Rules between Sets of Items in Large Databases. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pp. 207-216. ACM, 1993. doi:10.1145/170036.170072.
  • [2] Savasere A, Omieciński E, Navathe SB. An Efficient Algorithm for Mining Association Rules in Large Databases. In: Proceedings of 21th International Conference on Very Large Data Bases, pp. 432-444. Morgan Kaufmann, 1995.
  • [3] Park JS, Chen MS, Yu PS. An Effective Hash Based Algorithm for Mining Association Rules. In: Proceedings of the 1995 ACM SIGMOD International Conference on Management of Data, pp. 175-186. ACM Press, 1995. doi:10.1145/568271.223813.
  • [4] Rauch J. Observational Calculi and Association Rules, volume 469 of Studies in Computational Intelligence. Springer Berlin Heidelberg, 2013. ISBN 978-3-642-11736-7.
  • [5] Borgelt C. Simple Algorithms for Frequent Item Set Mining. In: Advances in Machine Learning II, volume 263 of Studies in Computational Intelligence, pp. 351-369. Springer Berlin Heidelberg, 2010. doi:10.1007/978-3-642-05179-1_16.
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  • [7] Herawan T, Deris MM. A Soft Set Approach for Association Rules Mining. Knowledge-Based Systems, 2011;24(1):186-195. doi:10.1016/j.knosys.2010.08.005.
  • [8] Geng L, Hamilton HJ. Interestingness Measures for Data Mining: A Survey. ACM Comput. Surv., 2006;38(3):32. doi:10.1145/1132960.1132963.
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  • [10] Wieczorek A, Słowiński R. Generating a Set of Association and Decision Rules with Statistically Representative Support and Anti-support. Information Sciences, 2014;277:56-70. doi:10.1016/j.ins.2014.02.003.
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  • [12] Bonates T, Hammer PL, Kogan A. Maximum Patterns in Datasets. Discrete Applied Mathematics, 2008;156(6):846-861. doi:10.1016/j.dam.2007.06.004.
  • [13] Moshkov M, Piliszczuk M, Zielosko B. Greedy Algorithm for Construction of Partial Association Rules. Fundamenta Informaticae, 2009;92(3):259-277. doi:10.3233/FI-2009-0074.
  • [14] Nguyen HS, Ślęzak D. Approximate Reducts and Association Rules - Correspondence and Complexity Results. In: Zhong N, Skowron A, Ohsuga S (eds.), RSFDGrC, volume 1711 of LNCS, pp. 137-145. Springer, 1999.
  • [15] Feige U. A Threshold of ln n for Approximating Set Cover. In: Journal of the ACM (JACM), volume 45, pp. 634-652. ACM New York, 1998. doi:10.1145/285055.285059.
  • [16] Pawlak Z, Skowron A. Rudiments of Rough Sets. Information Sciences, 2007;177(1):3-27. doi:10.1016/j.ins.2006.06.003.
  • [17] Agrawal R, Mannila H, Srikant R, Toivonen H, Verkamo AI. Fast Discovery of Association Rules. In: Fayyad UM, Piatetsky-Shapiro G, Smyth P, Uthurusamy R (eds.), Advances in Knowledge Discovery and Data Mining, pp. 307-328. American Association for Artificial Intelligence, 1996.
  • [18] Zaki MJ, Parthasarathy S, Ogihara M, Li W. New Algorithms for Fast Discovery of Association Rules. Technical report, Rochester, NY, USA, 1997.
  • [19] Zielosko B. Application of Dynamic Programming Approach to Optimization of Association Rules Relative to Coverage and Length. Fundamenta Informaticae, 2016;148(1-2):87-105. doi:10.3233/FI-2016-1424.
  • [20] Borgelt C, Kruse R. Induction of Association Rules: Apriori Implementation. In: 15th Conference on Computational Statistics (Compstat 2002, Berlin, Germany), pp. 395-400. Physica Verlag, Heidelberg, 2002.
  • [21] Moshkov M, Piliszczuk M, Zielosko B. On Construction of Partial Association Rules. In: Rough Sets and Knowledge Technology, 4th International Conference, volume 5589 of Lecture Notes in Computer Science, pp. 176-183. Springer, 2009. doi:10.1007/978-3-642-02962-2_22.
  • [22] Alsolami F, Amin T, Moshkov M, Zielosko B. Comparison of Heuristics for Optimization of Association Rules. In: Proceedings of the 24th International Workshop on Concurrency, Specification and Programming, Rzeszow, Poland, September 28-30, 2015. 2015 pp. 4-11.
  • [23] Asuncion A, Newman DJ. UCI Machine Learning Repository, http://www.ics.uci.edu/~mlearn/,accessed Feb. 2016.
  • [24] Alkhalid A, Amin T, Chikalov I, Hussain S, Moshkov M, Zielosko B. Dagger: A Tool for Analysis and Optimization of Decision Trees and Rules. In: Computational Informatics, Social Factors and New Information Technologies: Hypermedia Perspectives and Avant-Garde Experiences in the Era of Communicability Expansion, pp. 29-39. Blue Herons, 2011.
  • [25] Amin T, Chikalov I, Moshkov M, Zielosko B. Dynamic Programming Approach for Partial Decision Rule Optimization. Fundamenta Informaticae, 2012;119(3-4):233-248. doi:10.3233/FI-2012-735.
  • [26] Amin T, Chikalov I, Moshkov M, Zielosko B. Dynamic Programming Approach to Optimization of Approximate Decision Rules. Information Sciences, 2013;221:403-418. doi:10.1016/j.ins.2012.09.018.
  • [27] Zielosko B. Sequential Optimization of γ-decision Rules. In: Federated Conference on Computer Science and Information Systems, pp. 339-346. 2012.
  • [28] Zielosko B, Chikalov I, Moshkov M, Amin T. Optimization of Decision Rules Based on Dynamic Programming Approach. In: Innovations in Intelligent Machines (4), volume 514 of Studies in Computational Intelligence, pp. 369-392. Springer, 2014. doi:10.1007/978-3-319-01866-9-12.
  • [29] Moshkov M, Zielosko B. Combinatorial Machine Learning - A Rough Set Approach, volume 360 of Studies in Computational Intelligence. Springer, 2011. doi:10.1007/978-3-642-20995-6.
  • [30] Tan PN, Steinbach M, Karpatne A, Kumar V. Introduction to Data Mining (2Nd Edition). Pearson, 2nd edition, 2018.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-be1b3944-9f8d-4005-8075-ecf2add2c071
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