Greedy Algorithm for Optimization of Association Rules Relative to Length | SpringerLink
Skip to main content
Springer Nature Link
Log in

Greedy Algorithm for Optimization of Association Rules Relative to Length

  • Conference paper
  • First Online:
Intelligent Decision Technologies 2016 (IDT 2016)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 56))

Included in the following conference series:

  • 672 Accesses

Abstract

In the paper, an optimization of \(\alpha \)-Association rules constructed by greedy algorithm is proposed. It allows us to decrease the number of rules and obtain short rules, what is important from the point of view of knowledge representation. Experimental results for data sets from UCI Machine Learning Respository are presented.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 22879
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 28599
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
JPY 28599
Price includes VAT (Japan)
  • Durable hardcover 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

Similar content being viewed by others

References

  1. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Bocca, J.B., Jarke, M., Zaniolo, C. (eds.) VLDB, pp. 487–499. Morgan Kaufmann (1994)

    Google Scholar 

  2. Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: SIGMOD ’93, pp. 207–216. ACM (1993)

    Google Scholar 

  3. Borgelt, C.: Simple algorithms for frequent item set mining. In: Koronacki, J., Raś, Z.W., Wierzchoń, S.T., Kacprzyk, J. (eds.) Advances in Machine Learning II, Studies in Computational Intelligence, vol. 263, pp. 351–369. Springer, Berlin Heidelberg (2010)

    Google Scholar 

  4. Borgelt, C., Kruse, R.: Induction of association rules: Apriori implementation. 15th Conference on Computational Statistics (Compstat 2002. Berlin, Germany), pp. 395–400. Physica Verlag, Heidelberg (2002)

    Google Scholar 

  5. Feige, U.: A threshold of \(\ln n\) for approximating set cover. In: Leighton, F.T. (ed.) Journal of the ACM (JACM), vol. 45, pp. 634–652. ACM New York (1998)

    Google Scholar 

  6. Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Min. Knowl. Discov. 8(1), 53–87 (2004)

    Article  MathSciNet  Google Scholar 

  7. Herawan, T., Deris, M.M.: A soft set approach for association rules mining. Knowled.-Based Syst. 24(1), 186–195 (2011)

    Article  Google Scholar 

  8. Kozak, J., Boryczka, U.: Multiple boosting in the ant colony decision forest meta-classifier. Knowled.-Based Syst. 75, 141–151 (2015)

    Article  Google Scholar 

  9. Lichman, M.: UCI Machine Learning Repository. http://archive.ics.uci.edu/ml/. Accessed Feb 2016

  10. Moshkov, M.J., Skowron, A., Suraj, Z.: On minimal rule sets for almost all binary information systems. Fundam. Inform. 80(1–3), 247–258 (2007)

    MathSciNet  MATH  Google Scholar 

  11. Moshkov, M.J., Piliszczuk, M., Zielosko, B.: On construction of partial association rules. In: Wen, P., Li, Y., Polkowski, L., Yao, Y., Tsumoto, S., Wang, G. (eds.) RSKT, LNCS, vol. 5589, pp. 176–183. Springer (2009)

    Google Scholar 

  12. Moshkov, M.J., Piliszczuk, M., Zielosko, B.: Greedy algorithm for construction of partial association rules. Fundam. Inform. 92(3), 259–277 (2009)

    MathSciNet  MATH  Google Scholar 

  13. Nguyen, H.S., Ślȩzak, D.: Approximate reducts and association rules - correspondence and complexity results. In: Zhong, N., Skowron, A., Ohsuga, S. (eds.) RSFDGrC, LNCS, vol. 1711, pp. 137–145. Springer (1999)

    Google Scholar 

  14. Park, J.S., Chen, M.S., Yu, P.S.: An effective hash based algorithm for mining association rules. In: Carey, M.J., Schneider, D.A. (eds.) SIGMOD Conference, pp. 175–186. ACM Press (1995)

    Google Scholar 

  15. Pawlak, Z., Skowron, A.: Rudiments of rough sets. Inf. Sci. 177(1), 3–27 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  16. Rissanen, J.: Modeling by shortest data description. Automatica 14(5), 465–471 (1978)

    Article  MATH  Google Scholar 

  17. Savasere, A., Omiecinski, E., Navathe, S.B.: An efficient algorithm for mining association rules in large databases. In: Dayal, U., Gray, P.M.D., Nishio, S. (eds.) VLDB, pp. 432–444. Morgan Kaufmann (1995)

    Google Scholar 

  18. Skowron, A.: Rough sets in KDD - plenary talk. In: Shi, Z., Faltings, B., Musen, M. (eds.) Proceedings of the 16th IFIP, pp. 1–14. World Computer Congress, Publishing House of Electronic Industry (2000)

    Google Scholar 

  19. Slavík, P.: A tight analysis of the greedy algorithm for set cover. In: Proceedings of the Twenty-Eighth Annual ACM Symposium on Theory of Computing. pp. 435–441. ACM New York (1996)

    Google Scholar 

  20. Stańczyk, U.: Selection of decision rules based on attribute ranking. J. Intell. Fuzzy Syst. 29(2), 899–915 (2015)

    Article  MathSciNet  Google Scholar 

  21. Stefanowski, J., Vanderpooten, D.: Induction of decision rules in classification and discovery-oriented perspectives. Int. J. Intell. Syst. 16(1), 13–27 (2001)

    Article  MATH  Google Scholar 

  22. Tkacz, M.A.: Artificial neural networks in incomplete data sets processing. In: Kopotek, M.A., Wierzchoń, S.T., Trojanowski, K. (eds.) IIS: IIPWM’05. pp. 577–584. Advances in Soft Computing, Springer (2005)

    Google Scholar 

  23. Wieczorek, A., Słowiński, R.: Generating a set of association and decision rules with statistically representative support and anti-support. Inf. Sci. 277, 56–70 (2014)

    Article  Google Scholar 

  24. Zielosko, B.: Greedy algorithm for construction of partial association rules. Studia Inform. 31(2A), 225–236 (2010) (in Polish)

    Google Scholar 

  25. Zielosko, B.: Global optimization of exact association rules relative to coverage. In: Kryszkiewicz, M., Bandyopadhyay, S., Rybiński, H., Pal, S.K. (eds.) PReMI 2015. LNCS, vol. 9124, pp. 428–437. Springer (2015)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Computer Science, University of Silesia, 39, Bȩdzińska St., 41-200, Sosnowiec, Poland

    Beata Zielosko

  2. EL-PLUS, 11, Inwalidzka St., 41-506, Chorzów, Poland

    Marek Robaszkiewicz

Authors
  1. Beata Zielosko
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marek Robaszkiewicz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Beata Zielosko .

Editor information

Editors and Affiliations

  1. Maritime University, Gdynia, Poland

    Ireneusz Czarnowski

  2. Artificial Intelligence Department, Universidad Politécnica de Madrid, Boadilla del Monte, Madrid, Spain

    Alfonso Mateos Caballero

  3. KES International, Shoreham-by-sea, United Kingdom

    Robert J. Howlett

  4. University of Canberra, Canberra, South Australia, Australia

    Lakhmi C. Jain

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Zielosko, B., Robaszkiewicz, M. (2016). Greedy Algorithm for Optimization of Association Rules Relative to Length. In: Czarnowski, I., Caballero, A., Howlett, R., Jain, L. (eds) Intelligent Decision Technologies 2016. IDT 2016. Smart Innovation, Systems and Technologies, vol 56. Springer, Cham. https://doi.org/10.1007/978-3-319-39630-9_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-39630-9_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39629-3

  • Online ISBN: 978-3-319-39630-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics