Skip to main content
SpringerLink
Log in
SpringerLink
Log in

An Improved Algorithm for Mining Top-k Association Rules

  • Conference paper
  • First Online:
Advanced Computational Methods for Knowledge Engineering (ICCSAMA 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 629))

  • 889 Accesses

Abstract

This paper proposes an improved algorithm of TopKRules algorithm which was proposed by Philippe et al. in 2012 to mine top-k association rules (ARs). To impove the perfomance of TopKRules, we develop two propositions to reduce search space and runtime in the mining process. Experimental results on standard databases show that our algorithm need less time than TopKRules algorithm to generate usefull rules.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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

References

  1. Agrawal, R., Imielminski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings ACM International Conference on Management of Data, pp. 207–216. ACM Press (1993)

    Google Scholar 

  2. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of the 20th International Conference on Very Large Data Bases, pp. 487–499 (1994)

    Google Scholar 

  3. Deng, Z., Fang, G.: Mining top-rank-k frequent patterns. In: ICMLC 2007, pp. 851–856 (2007)

    Google Scholar 

  4. Deng, Z.H.: Fast mining top-rank-k frequent patterns by using node-lists. Expert Syst. Appl. 41(4), 1763–1768 (2014)

    Article  Google Scholar 

  5. Fang, G., Deng, Z.H.: VTK: vertical mining of top-rank-k frequent patterns. In: FSKD 2008, pp. 620–624 (2008)

    Google Scholar 

  6. Fournier-Viger, P., Wu, C.-W., Tseng, V.S.: Mining top-k association rules. In: Proceedings of the 25th Canadian Conference on Artificial Intelligence (AI 2012). LNAI, vol. 7310, pp. 61–73. Springer, Heidelberg (2012)

    Google Scholar 

  7. Han, J., Pei, H., Yin, Y.: Mining frequent patterns without candidate generation. Data Min. Knowl. Disc. 8(1), 53–87 (2004)

    Article  Google Scholar 

  8. Huynh-Thi-Le, Q., Le, T., Vo, B., Le, B.: An efficient and effective algorithm for mining top-rank-k frequent patterns. Expert Syst. Appl. 42(1), 156–164 (2015)

    Article  Google Scholar 

  9. Le, T., Vo, B.: An N-list-based algorithm for mining frequent closed patterns. Expert Syst. Appl. 42(19), 6648–6657 (2015)

    Article  Google Scholar 

  10. Nguyen, L.T.T., Trinh, T., Nguyen, N.T., Vo, B.: A method for mining top-rank-k frequent closed itemsets. J. Intell. Fuzzy Syst. 32(2), 1297–1305 (2017)

    Article  Google Scholar 

  11. Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L.: Efficient mining of association rules using closed itemset lattices. Inf. Syst. 24(1), 25–46 (1999)

    Article  MATH  Google Scholar 

  12. Pietracaprina, A., Vandin, F.: Efficient incremental mining of top-k frequent closed itemsets. In: Tenth International Conference Discovery Science, pp. 275–280. Springer, Heidelberg (2004)

    Google Scholar 

  13. Sahoo, J., Das, A.K., Goswami, A.: An effective association rule mining scheme using a new generic basis. Knowl. Inf. Syst. 43(1), 127–156 (2015)

    Article  Google Scholar 

  14. Saif-Ur-Rehman, J., Ashraf, J., Habib, A., Salam, A.: Top-k miner: top-k identical frequent itemsets discovery without user support threshold. Knowl. Inf. Syst. 48(3), 741–762 (2016)

    Article  Google Scholar 

  15. Tzvetkov, P., Yan, X., Han, J.: TSP: mining top-k closed sequential patterns. Knowl. Inf. Syst. 7(4), 438–457 (2005)

    Article  Google Scholar 

  16. Vo, B., Hong, T.P., Le, B.: DBV-miner: a dynamic bit-vector approach for fast mining frequent closed itemsets. Expert Syst. Appl. 39(8), 7196–7206 (2012)

    Article  Google Scholar 

  17. Vo, B., Hong, T.P., Le, B.: A lattice-based approach for mining most generalization association rules. Knowl.-Based Syst. 45, 20–30 (2013)

    Article  Google Scholar 

  18. Vo, B., Le, B.: Mining traditional association rules using frequent itemsets lattice. In: International Conference on Computers and Industrial Engineering, pp. 1401–1406. IEEE Press (2009)

    Google Scholar 

  19. Vo, B., Le, B.: Interestingness measures for association rules: combination between lattice and hash tables. Expert Syst. Appl. 38(9), 11630–11640 (2011)

    Article  Google Scholar 

  20. Webb, G.I., Zhang, S.: K-optimal rule discovery. Data Min. Knowl. Disc. 10(1), 39–79 (2005)

    Article  MathSciNet  Google Scholar 

  21. Webb, G.I.: Filtered top-k association discovery. WIREs Data Mining Knowl. Discov. 1(3), 183–192 (2011)

    Article  Google Scholar 

  22. You, Y., Zhang, J., Yang, Z., Liu, G.: Mining top-k fault tolerant association rules by redundant pattern disambiguation in data streams. In: International Conference Intelligent Computing and Cognitive Informatics, pp. 470–473. IEEE Press (2010)

    Google Scholar 

  23. Zaki, M.J.: Mining non-redundant association rules. Data Min. Knowl. Disc. 9(3), 223–248 (2004)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgments

This work was carried out during the tenure of an ERCIM ‘Alain Bensoussan’ Fellowship Programme.

Author information

Authors and Affiliations

  1. Faculty of Information Technology, Dong An Polytechnic, Binh Duong, Vietnam

    Linh T. T. Nguyen

  2. Faculty of Information Technology, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam

    Loan T. T. Nguyen

  3. Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland

    Loan T. T. Nguyen

  4. Faculty of Information Technology, Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam

    Bay Vo

Authors
  1. Linh T. T. Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Loan T. T. Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bay Vo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bay Vo .

Editor information

Editors and Affiliations

  1. Department of Informatics, Humboldt-Universität zu Berlin, Berlin, Germany

    Nguyen-Thinh Le

  2. Department of Networked Systems and Services, Budapest University of Technology and Economics, Budapest, Hungary

    Tien van Do

  3. Department of Information Systems, Wrocław University of Science and Technology, Wroclaw, Poland

    Ngoc Thanh Nguyen

  4. Theoretical and Applied Computer Science Laboratory, University of Lorraine, Metz, France

    Hoai An Le Thi

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

Nguyen, L.T.T., Nguyen, L.T.T., Vo, B. (2018). An Improved Algorithm for Mining Top-k Association Rules. In: Le, NT., van Do, T., Nguyen, N., Thi, H. (eds) Advanced Computational Methods for Knowledge Engineering. ICCSAMA 2017. Advances in Intelligent Systems and Computing, vol 629. Springer, Cham. https://doi.org/10.1007/978-3-319-61911-8_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-61911-8_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61910-1

  • Online ISBN: 978-3-319-61911-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation