Skip to main content
SpringerLink
Log in

Learning Association Rules from Data through Domain Knowledge and Automation

  • Conference paper
Rules on the Web. From Theory to Applications (RuleML 2014)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 8620))

Abstract

An approach to automated data mining with association rules based on domain knowledge is introduced. Association rules are understood as interesting pairs of general Boolean attributes. Items of domain knowledge corresponding to various relations of non-Boolean attributes are used to formulate reasonable analytical questions. Particular items of knowledge are mapped to sets of association rules which can be considered their consequences. The sets of consequences are then used to interpret sets of association rules resulting from a data mining procedure.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Agrawal, R., Imielinski, T., Swami, A.: Mining Associations between Sets of Items in Large Databases. In: Buneman, P., Jajodia, S. (eds.) Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pp. 207–216. ACM Press, Fort Collins (1993)

    Chapter  Google Scholar 

  2. Atzmüller, M., Puppe, F., Buscher, H.P.: Exploiting Background Knowledge for Knowledge-Intensive Subgroup Discovery. In: Kaelbling, L.P., Saffiotti, A. (eds.) Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, IJCAI 2005, Edinburgh, Scotland, UK, pp. 647–652 (2005)

    Google Scholar 

  3. Atzmüller, M., Puppe, F., Buscher, H.P.: A Semi-Automatic Approach for Confounding-Aware Subgroup Discovery. International Journal on Artificial Intelligence Tools 18, 81–98 (2009)

    Article  Google Scholar 

  4. Aumann, Y., Lindell, Y.: A Statistical Theory for Quantitative Association Rules. J. Intell. Inf. Syst. 20, 255–283 (2003)

    Article  Google Scholar 

  5. Hájek, P., Havel, I., Chytil, M.: The GUHA method of automatic hypothesis determinantion. Computing 1, 293–308 (1966)

    Article  MATH  Google Scholar 

  6. Hájek, P., Havránek, T.: Mechanising Hypothesis Formation - Mathematical Foundations for a General Theory. Springer, Heidelberg (1978)

    Book  Google Scholar 

  7. Hájek, P.: The new version of the GUHA procedure ASSOC. In: Proceedings of COMPSTAT 1984, pp. 360–365 (1984)

    Google Scholar 

  8. Hájek, P., Havránek, T.: GUHA 80: An Application of Artificial Intelligence to Data Analysis. Computers and Artificial Intelligence 1, 107–134 (1982)

    Google Scholar 

  9. Hájek, P., Holeňa, M., Rauch, J.: The GUHA method and its meaning for data mining. J. Comput. Syst. Sci. 76, 34–48 (2010)

    Article  MATH  Google Scholar 

  10. Hájek, P., Ivánek, J.: Artificial Intelligence and Data Analysis. In: Caussinus, H., Ettinger, P., Tomassone, R. (eds.) Proceedings COMPSTAT 1982, pp. 54–60. Physica Verlag, Wien (1982)

    Google Scholar 

  11. Ierusalimschy, R., Figueiredo, L.H., de Celes, W.: Lua – an extensible extension language. Software: Practice & Experience 26, 635–652 (1996)

    Google Scholar 

  12. Jaroszewicz, S., Simovici, D.A.: Interestingness of frequent itemsets using Bayesian networks as background knowledge. In: Kim, W., et al. (eds.) Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle, Washington, USA, pp. 178–186 (2004)

    Google Scholar 

  13. Jaroszewicz, S., Scheffer, T., Simovici, D.A.: Scalable pattern mining with Bayesian networks as background knowledge. Data Min. Knowl. Discov. 18, 56–100 (2009)

    Article  MathSciNet  Google Scholar 

  14. Lavrac, N., et al.: The utility of background knowledge in learning medical diagnostic rules. Applied Artificial Intelligence 7, 273–293 (1993)

    Article  Google Scholar 

  15. Mansingh, G., Osei-Bryson, K.-M., Reichgelt, H.: Using ontologies to facilitate post-processing of association rules by domain experts. Information Sciences 181, 419–434 (2011)

    Article  Google Scholar 

  16. Phillips, J., Buchanan, B.G.: Ontology guided knowledge discovery in databases. In: Proc. First International Conference on Knowledge Capture, pp. 123–130. ACM, Victoria (2001)

    Google Scholar 

  17. Rauch, J.: Considerations on Logical Calculi for Dealing with Knowledge in Data Mining. In: Ras, Z.W., Dardzinska, A. (eds.) Advances in Data Management. SCI, vol. 223, pp. 177–199. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  18. Rauch, J.: Formalizing Data Mining with Association Rules. In: Proceedings of 2012 IEEE International Conference on Granular Computing (GRC 2012), pp. 406–411. IEEE Computer Society, Los Alamitos (2012)

    Chapter  Google Scholar 

  19. Rauch, J.: Observational Calculi and Association Rules, p. 296. Springer, Berlin (2013)

    Book  MATH  Google Scholar 

  20. Rauch, J.: Formal Framework for Data Mining with Association Rules and Domain Knowledge – Overview of an Approach Observational Calculi and Association Rules. To appear in Fundamenta Informaticae

    Google Scholar 

  21. Rauch, J., Šimůnek, M.: An Alternative Approach to Mining Association Rules. In: Lin, T.Y., et al. (eds.) Data Mining: Foundations, Methods, and Applications. SCI, vol. 6, pp. 211–231. Springer (2005)

    Google Scholar 

  22. Rauch, J., Šimůnek, M.: Applying Domain Knowledge in Association Rules Mining Process - First Experience. In: Kryszkiewicz, M., Rybinski, H., Skowron, A., Raś, Z.W., et al. (eds.) ISMIS 2011. LNCS (LNAI), vol. 6804, pp. 113–122. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  23. Suzuki, E.: Undirected Discovery of Interesting Exception Rules. International Journal of Pattern Recognition and Artificial Intelligence 16(8), 1065–1086 (2002)

    Article  Google Scholar 

  24. Šimůnek, M.: Academic KDD Project LISp-Miner. In: Abraham, A., Franke, K., Köppen, M. (eds.) Intelligent Systems Design and Applications. AISC, vol. 23, pp. 263–272. Springer, Heidelberg (2003)

    Google Scholar 

  25. Šimůnek, M.: LISp-Miner Control Language – description of scripting language implementation. Journal of System Integration 5(2) (2014), http://www.si-journal.org/index.php/JSI/article/view/193

  26. Šimůnek, M., Rauch, J.: EverMiner – Towards Fully Automated KDD Process. In: Funatsu, K., Hasegava, K. (eds.) New Fundamental Technologies in Data Mining, pp. 221–240. InTech, Rijeka (2011)

    Google Scholar 

  27. Šimůnek, M., Rauch, J.: EverMiner Prototype using LISp-Miner Control Language. In: Andreasen, T., Christiansen, H., Cubero, J.-C., Raś, Z.W. (eds.) ISMIS 2014. LNCS (LNAI), vol. 8502, pp. 113–122. Springer, Heidelberg (2014), http://isl.ruc.dk/ismis2014/

    Google Scholar 

  28. Sharma, S., Osei-Bryson, K.-M.: Toward an integrated knowledge discovery and data mining process model. The Knowledge Engineering Review 25, 49–67 (2010)

    Article  Google Scholar 

  29. Tan, P.-N., Kumar, V., Srivastava, J.: Selecting the right objective measure for association analysis. Information Systems 29, 293–313 (2004)

    Article  Google Scholar 

  30. Vavpetic, A., Podpecan, V., Lavrac, N.: Semantic subgroup explanations. J. Intell. Inf. Syst. 42, 233–254 (2014)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Informatics and Statistics, University of Economics, nám W. Churchilla 4, 130 67, Prague 3, Czech Republic

    Jan Rauch & Milan Šimůnek

Authors
  1. Jan Rauch
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Milan Šimůnek
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Information Studies, University College London, Gower Street, WC1E 6BT, London, UK

    Antonis Bikakis

  2. Department of Computer Science, Stony Brook University, 11794, Stony Brook, NY, USA

    Paul Fodor

  3. SINTEF, University of Oslo, Forskningsveien 1, 0314, Oslo, Norway

    Dumitru Roman

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Rauch, J., Šimůnek, M. (2014). Learning Association Rules from Data through Domain Knowledge and Automation. In: Bikakis, A., Fodor, P., Roman, D. (eds) Rules on the Web. From Theory to Applications. RuleML 2014. Lecture Notes in Computer Science, vol 8620. Springer, Cham. https://doi.org/10.1007/978-3-319-09870-8_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-09870-8_20

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09869-2

  • Online ISBN: 978-3-319-09870-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation