Skip to main content
SpringerLink
Log in

Association rule mining using treap

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

The analytical process designed to mine data became more difficult with the rapid information explosion. This in-turn created completely distributed and un-indexed data. Thus assessing and finding relations between variables from large database became a tedious task. There are various association rule mining algorithms available for this process, but a powerful association algorithm which runs in reduced time and space complexity is hard to find. In this work, we propose a new rule mining algorithm which works in a priority model for finding interesting relations in a database using the data structure Treap. While comparing with Apriori’s O (en) and FP growth’s O (n2), the proposed algorithm finishes mining in O (n) in its best case analysis and in O (n log n) in its worst case analysis. This was found to be much better when compared to other algorithms of its kind. The results were evaluated and compared with the existing algorithm.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Suneetha KR, Krishnamoorti R (2010) Advanced version of a priori algorithm. In: Proceedings of IEEE-ICIIC, pp 238–245

  2. Pei J (2002) Pattern growth methods for frequent pattern mining. In: thesis submitted for Doctor of Philosophy, Simon Fraser University, pp 99–134

  3. Boney L, Tewfik AH, Hamdy KN (2006) Minimum association rule in large Database. In: Proceedings of Third IEEE International Conference on Computing, pp 12–16

  4. Agarwal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceedings of VLDB, pp 487–499

  5. Bodon F (2003) A fast apriori implementation. In: Proceedings of IEEE ICDM workshop on frequent item set mining implementation, vol 9

  6. Borgelt C (2004) Recursion pruning for the apriori algorithm. In: Proceedings of 2nd IEEE ICDM workshop on frequent item set mining implementations, vol 126

  7. Zaki M, Parthasarathy S, Ogihara M, Li W (1997) New algorithms for fast discovery of association rules. In: Proceedings of 3rd international conference on knowledge discovery and data mining, vol 2, pp 283–296

  8. Anandhavalli, Gautaman K (2007) Association rule mining in genomics. Int J Comput Theory Eng, vol 1

  9. Cooper C, Zito M (2007) Realistic synthetic data for testing association rule mining algorithms for market basket databases. Knowl Discov Databases: PKDD 9:398–405

    Google Scholar 

  10. Varde AS, Takahashi M, Rundensteiner EA, Ward MO, Maniruzzaman M, Sisson RD (2004) Apriori algorithm and game of life for predictive analysis in materials science. Int J Knowl Based Intell Eng Syst 8:116–122

    Google Scholar 

  11. Wu H, Lu Z, Pan L, Xu R, Jiang W (2009) An improved apriori based algorithm for association rules mining. In: Proceedings of sixth international conference on fuzzy systems and knowledge discovery, pp 51–55

  12. Sun D, Teng S, Zhang W, Zhu H (2007) An algorithm to improve the effectiveness of apriori. In: Proceedings of 6th IEEE international conference on cognitive informatics, vol 1, pp 385–390

  13. Bodon F (2003) A fast apriori implementation. In: Proceedings of IEEE ICDM workshop on frequent item-set mining implementation, vol 9

  14. Kryszkiewicz M, Rybiński H (2000) Data mining in incomplete information systems from rough set perspective. Rough Set Methods Appl 56:567–580

    Article  MathSciNet  MATH  Google Scholar 

  15. Kosters AW, Marchiori E, Oerlrmans AJ (1999) Mining clusters with association rules. Third symposium on intelligent data analysis. In: Proceedings of Springer Lecture Notes in Computer Science, pp 39–50

  16. Lin TY (1996) Rough set theory in very large databases. Symp Model, Anal Simul 2:936–941

    Google Scholar 

  17. Borgelt C (2005) An implementation of FP growth algorithm. In: Proceedings of workshop on open source mining software ACM

  18. Malik K, Raheja N, Garg P (2011) Enhance FP growth algorithm. Int J Comput Eng Manag 12:54–57

    Google Scholar 

  19. Anand HS, Vinodchandra SS (2014) Horizontal and vertical rule mining algorithms, ACCIS. In: Proceedings of Elsevier, pp 26–28

  20. Vinodchandra SS, Hareendran S (2014) Artificial intelligence and machine learning, 1st edn. PHI publishers, Delhi

  21. Guy EB, Margaret RM (1998) Fast set operations using treaps. In: Proceedings of the tenth annual ACM symposium on parallel algorithms and architectures, pp 16–26

  22. Aragon CR, Aragon C (1996) randomized search trees. Algorithmica 16:464–497

    Article  MathSciNet  MATH  Google Scholar 

  23. Mayadevi N, Vinodchandra SS, Ushakumari S (2015) SCADA based operator support system for power plant fault diagnosis. ACIDS-2015. In: Proceedings of Springer, pp 23–26

  24. Mayadevi N, Vinodchandra SS, Ushakumari S (2014) Expert system for power plant operator performance evaluation. In: IEEE ICACC, pp 27–29

  25. Wu H, Lu Z, Pan L, Xu R, Jiang W (2009) An improved apriori based algorithm for association rules mining. In: Proceedings of sixth international conference on fuzzy systems and knowledge discovery, pp 51–55

  26. Das R, Bhattacharyya DK, Kalita JK (2010) Clustering gene expression data using an effective dissimilarity measure. Int J Comput Biosci 1:55–68

    Article  Google Scholar 

  27. Wang Xizhao, Hong Jiarong (1999) Learning optimization in simplifying fuzzy rules. Fuzzy Sets Syst 106(3):349–356

    Article  MathSciNet  MATH  Google Scholar 

  28. Wang Xizhao, Wang Yadong, Xiaofei Xu, Ling Weide, Yeung Daniel (2001) A new approach to fuzzy rule generation: fuzzy extension matrix. Fuzzy Sets Syst 123(3):291–306

    Article  MathSciNet  MATH  Google Scholar 

  29. Wang Xizhao, Dong Chunru, Fan Tiegang (2007) Training T-S norm neural networks to refine weights for fuzzy if-then rules. Neurocomputing 70(13–15):2581–2587

    Article  Google Scholar 

  30. Wang Xizhao, Dong Chunru (2009) Improving generalization of fuzzy if-then rules by maximizing fuzzy entropy. IEEE Trans Fuzzy Syst 17(3):556–567

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgments

The authors extend their sincere gratitude to Maniyar power plant operators and technicians who helped whole heartedly while collecting the datasets from the plant during the various stages of the work. We also thank Kerala water authority for giving us the water samples as datasets for carrying out the experiment. We also extend our heartfelt thanks towards Kerala State IT Mission-Government of Kerala for providing the fund for this work.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, College of Engineering Trivandrum, Thiruvananthapuram, India

    H. S. Anand

  2. Computer Center, University of Kerala, Thiruvananthapuram, India

    S. S. Vinodchandra

Authors
  1. H. S. Anand
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. S. Vinodchandra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to H. S. Anand.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Anand, H.S., Vinodchandra, S.S. Association rule mining using treap. Int. J. Mach. Learn. & Cyber. 9, 589–597 (2018). https://doi.org/10.1007/s13042-016-0546-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-016-0546-7

Keywords

Search

Navigation