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Expert deduction rules in data mining with association rules: a case study

  • Jan Rauch
Regular Paper
  • 62 Downloads

Abstract

An approach to dealing with domain knowledge in data mining with association rules is introduced. We deal with association rules with remarkably enhanced syntax. This opens various possibilities for both logical and expert deduction. An expert deduction rule is a logically incorrect deduction rule which is supported by an indisputable fact concerning the application domain. The expert deduction rule is correct according to the given indisputable fact if a suitable assertion related to the given expert deduction rule can be formally proved from this indisputable fact. Examples of expert deduction rules and their applications are presented.

Keywords

Data mining Association rules Domain knowledge Logical calculus of association rules Deduction rules Expert deduction rules 

Notes

Acknowledgements

The work described here has been supported by funds of institutional support for long-term conceptual development of science and research at the Faculty of Informatics and Statistics of the University of Economics, Prague.

References

  1. 1.
    Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Bocca JB, Jarke M, Zaniolo C (eds) Proceedings of 20th international conference on very large data bases, VLDB, vol 1215. Morgan Kaufmann, San Francisco, pp 487–499Google Scholar
  2. 2.
    Brossette SE, Sprague AP, Hardin JM, Waites KB, Jones WT, Moser SA (1998) Association rules and data mining in hospital infection control and public health surveillance. J Am Med Inf Assoc (JAMIA) 5(4):373–381CrossRefGoogle Scholar
  3. 3.
    Delgado M, Sanchez D, Martin-Bautista MJ, Vila MA (2001) Mining association rules with improved semantics in medical databases. Artif Intell Med 21(1–3):241–245CrossRefGoogle Scholar
  4. 4.
    Fürnkranz J, Kliegr T (2015) A brief overview of rule learning. In: Bassiliades N, Gottlob G, Sadri F, Paschke A, Roman D (eds)In:9th international symposium on foundations, tools, and application, RuleML 2015. LNCS, vol 9202. Springer, Heidelberg, pp 56–69Google Scholar
  5. 5.
    Geng L, Hamilton HJ (2006) Interestingness measures for data mining: a survey. ACM Comput Surv (CSUR) 38:1–32CrossRefGoogle Scholar
  6. 6.
    Hahsler M, Buchta Ch, Gruen B, Hornik K (Aug. 2017) arules: Mining Association Rules and Frequent Itemsets. R package version 1.3-1. http://CRAN.R-project.org/package=arules, cited 28 Aug. 2017
  7. 7.
    Brin S, Rastogi R, Kyuseok S (2003) Mining optimized gain rules for numeric attributes. IEEE Trans Knowl Data Eng 15(2):324–338CrossRefGoogle Scholar
  8. 8.
    Fukuda T, Morimoto Y, Morishita S, Tokuyama T (1999) Mining optimized association rules for numeric attributes. J Comput Syst Sci 58(1):1–12MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Gasmi G, Yahia SB, Nguifo EM, Bouker S (2007) Extraction of Association Rules Based on Literalsets. In: Song IY, EderTho J, Nguyen M (eds) In: DaWaK 2007. LNCS, vol 4654. Springer, Heidelberg, pp 293–302Google Scholar
  10. 10.
    Hájek P (1978) (guest ed. ) International Journal of Man-Machine Studies, special issue on GUHA. 10Google Scholar
  11. 11.
    Hájek P (1984) The new version of the GUHA procedure ASSOC. In: Havranek T, Sidak Z, Novak M (eds) In: COMPSTAT 1984. Springer, Heidelberg, pp 360–365Google Scholar
  12. 12.
    Hamrouni T, Yahia BS, Nguifo EM (2010) Generalization of association rules through disjunction. Ann Math Artif Intell 59(2):201–222MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Hájek P, Havel I, Chytil M (1966) The GUHA method of automatic hypotheses determination. Computing 1:293–308CrossRefzbMATHGoogle Scholar
  14. 14.
    Hájek P, Havránek T (Aug. 2016) Mechanising hypothesis formation—mathematical foundations for a general theory, Springer, Berlin, 1978, http://www.cs.cas.cz/hajek/guhabook/, cited 28 Aug 2016
  15. 15.
    Hájek P, Sochorová A, Zvárová J (1995) GUHA for personal computers. Comput Stat Data Anal 19:149–153CrossRefzbMATHGoogle Scholar
  16. 16.
    Hájek P, Holeňa M, Rauch J (2010) The GUHA method and its meaning for data mining. J Comput Syst Sci 76(1):34–48MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Idoudi R, Ettaba KS, Solaiman B, Hamrouni K (2016) Ontology knowledge mining based association rules ranking. In: Howlett RJ, Jain CL, Gabrys B, Toro C, Lim CP (eds) Knowledge-based and intelligent information & engineering systems. Procedia computer science, vol 96. Elsevier, Amsterdam, pp 345–354Google Scholar
  18. 18.
    Mansingh G, Osei-Bryson K-M, Reichgelt H (2011) Using ontologies to facilitate post-processing of association rules by domain experts. Inf Sci 181(3):419–434CrossRefGoogle Scholar
  19. 19.
    Marinica C, Guillet F (2010) Knowledge-based interactive postmining of association rules using ontologies. IEEE Trans Knowl Data Eng 22(6):784–797CrossRefGoogle Scholar
  20. 20.
    Minaei-Bidgoli B, Barmaki R, Nasiri M (2013) Mining numerical association rules via multi-objective genetic algorithms. Inf Sci 233:15–24CrossRefGoogle Scholar
  21. 21.
    Ordonez C, Ezquerra N, Santana CA (2006) Constraining and summarizing association rules in medical data. Knowl Inf Syst (KAIS) 9(3):259–283CrossRefGoogle Scholar
  22. 22.
    Personeni G, Bresso E, Devignes M, Dumontier M, Smaïl-Tabbone M, Coulet A (2017) Discovering associations between adverse drug events using pattern structures and ontologies. J Biomed Semant 8(1):29:1–29:13CrossRefGoogle Scholar
  23. 23.
    Qiang Y, Xindong W (2006) 10 challenging problems in data mining research. Int J Inf Technol Decis Mak 5(4):597–604CrossRefGoogle Scholar
  24. 24.
    Brin S, Rastogi R, Kyuseok S (1999) Mining optimized gain rules for numeric attributes. In: Usama Fayyad U, Chaudhuri S, Madigan D (eds) Proceedings on fifth ACM SIGKDD international conference on knowledge discovery and data mining. ACM Press, pp 135–144Google Scholar
  25. 25.
    Ralbovský M, Kuchař T (2007) Using disjunctions in association mining. In: Perner P (ed) Proceedings on advances in data mining—theoretical aspects and applications, LNCS, vol 4597. Springer, Berlin, pp 339–351Google Scholar
  26. 26.
    Rauch J (2005) Logic of association rules. Appl Intell 22:9–28CrossRefzbMATHGoogle Scholar
  27. 27.
    Rauch J (2013) Observational calculi and association rules. Springer, BerlinCrossRefzbMATHGoogle Scholar
  28. 28.
    Rauch J (2015) Formal framework for data mining with association rules and domain knowledge overview of an approach. Fund Inf 137:171–217MathSciNetzbMATHGoogle Scholar
  29. 29.
    Rauch J (2016) Logical aspects of dealing with domain knowledge in data mining with association rules. Fund Inf 148:1–33MathSciNetzbMATHGoogle Scholar
  30. 30.
    Rauch J, Šim\(\mathring{{\rm u}}\)nek M (2014) Learning association rules from data through domain knowledge and automation. In: Bikakis A, Fodor P, Roman D (eds.) RuleML 2014: Rules on the Web. From Theory to Applications. LNCS, vol. 8620, Springer, Heidelberg, pp 266–280Google Scholar
  31. 31.
    Rauch J, Šim\(\mathring{{\rm u}}\)nek M (2017) Apriori and GUHA comparing two approaches to data mining with association rules. Intell Data Anal 21:981–1013Google Scholar
  32. 32.
    Regulski K (2017) Formalization of technological knowledge in the field of metallurgu using document classification tools supported with semantic techniques. Arch Metall Mater 62(2):715–720CrossRefGoogle Scholar
  33. 33.
    Singh V, Nagpal S (2010) Integrating users domain knowledge with association rule mining. Int J Comput Sci Issues 7(2):30–34Google Scholar
  34. 34.
    Srikant R, Agrawal R (1997) Mining generalized association rules. Future Gen Comput Syst 13(2–3):161–180CrossRefGoogle Scholar
  35. 35.

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of EconomicsPragueCzech Republic

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