Skip to main content
SpringerLink
Log in

Detecting Definite Least Association Rule in Medical Database

  • Conference paper
  • First Online:
Proceedings of the First International Conference on Advanced Data and Information Engineering (DaEng-2013)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 285))

Abstract

Least association rule refers to the rule that only rarely occur in database but they might reveal some interesting knowledge in certain domain applications. In certain medical datasets, finding these rules is very important and required further analysis. In this paper we applied our novel measure known as Definite Factor (DF) with SLP-Growth algorithm to mining the Definite Least Association Rule (DELAR) from a benchmarked medical datasets. DELAR is also highly correlated and evaluated based on standard Lift measure. The result shows that DF can be used as alternative measure in capturing the interesting rules and thus verify its scalability.

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 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Agrawal, R., Imielinski, T., and Swami, A.: Database Mining: A Performance Perspective. IEEE Transactions on Knowledge and Data Engineering, 5 (6), 914-925 (1993)

    Google Scholar 

  2. Abdullah, Z., Herawan, T. and Deris, M.M.: Mining Significant Least Association Rules using Fast SLP-Growth Algorithm. In T.H. Kim and H. Adeli (Eds.): AST/UCMA/ISA/ACN 2010, LNCS, vol. 6059, pp. 324–336 (2010)

    Google Scholar 

  3. Z. Abdullah, T. Herawan and M.M. Deris. (2010). Scalable Model for Mining Critical Least Association Rules. In Rongbo Zhu et al. ICICA 2010, LNCS, vol. 6377, pp. 509–516. Springer Heidelberg (2010)

    Google Scholar 

  4. Abdullah, Z., Herawan, T., Noraziah, A., and Deris, M.M.: Extracting Highly Positive Association Rules from Students’ Enrolment Data. Procedia Social and Behavioral Sciences, 28, 107–111 (2011)

    Google Scholar 

  5. Abdullah, Z., Herawan, T., Noraziah, A., and Deris, M.M.: Mining Significant Association Rules from Educational Data using Critical Relative Support Approach. Procedia Social and Behavioral Sciences, 28, 97–101 (2011)

    Google Scholar 

  6. Herawan, T., Vitasari, P., and Abdullah, Z.: Mining Interesting Association Rules of Student Suffering Mathematics Anxiety. In J.M. Zain et al. (Eds.): ICSECS 2011, CCIS, vol. 188, part II, pp. 495–508. Springer Heidelberg (2011)

    Google Scholar 

  7. Herawan, T., Yanto, I.T.R., and Deris, M.M.: SMARViz: Soft maximal association rules visualization. In H. Badioze Zaman et al. (Eds.): IVIC 2009, LNCS, vol. 5857, pp. 664–674. Springer Heidelberg (2009)

    Google Scholar 

  8. Abdullah, Z., Herawan, T., and Deris, M.M.: Visualizing the Construction of Incremental Disorder Trie Itemset Data Structure (DOSTrieIT) for Frequent Pattern Tree (FP-Tree). In H.B. Zaman et al. (Eds.): IVIC 2011, LNCS, vol. 7066, pp. 183–195. Springer Heidelberg (2011)

    Google Scholar 

  9. Ahmad, N., Abdullah, Z., Herawan, T., and Deris, M.M.: WLAR-Viz: Weighted Least Association Rules Visualization. In B. Liu et al. (Eds): ICICA 2012, LNCS, vol.7473, pp.592–599. Springer Heidelberg (2012)

    Google Scholar 

  10. Herawan, T., Yanto, I.T.R., and Deris, M.M.: Soft Set Approach for Maximal Association Rules Mining. In D. Ślęzak et al. (Eds.): DTA 2009, CCIS, vol. 64, pp. 163–170 (2009)

    Google Scholar 

  11. Herawan, T., and Deris, M.M.: A Soft Set Approach for Association Rules Mining. Knowledge Based Systems, 24 (1), 186–195 (2011)

    Google Scholar 

  12. Abdullah, Z., Herawan, T., and Deris, M.M.: An Alternative Measure for Mining Weighted Least Association Rule and Its Framework. In J.M. Zain et al. (Eds.): ICSECS 2011, CCIS, vol. 188, part II, pp. 475–485. Springer Heidelberg (2011)

    Google Scholar 

  13. Abdullah, Z., Herawan, T., and Deris, M.M.: Efficient and Scalable Model for Mining Critical Least Association Rules. In a special issue from AST/UCMA/ISA/ACN 2010, Journal of The Chinese Institute of Engineer, 35 (4), 547–554 (2012)

    Google Scholar 

  14. Abdullah, Z., Herawan, T., Ahmad, N., and Deris, M.M.: DFP-Growth: An Efficient Algorithm for Mining Pattern in Dynamic Database. In B. Liu et al. (Eds): ICICA 2012, LNCS, vol. 7473, pp. 51–58. Springer Heidelberg (2012)

    Google Scholar 

  15. Ahmad, N., Abdullah, Z., Herawan, T., and Deris, M.M.: Scalable Technique to Discover Items Support from Trie Data Structure. In B. Liu et al. (Eds): ICICA 2012, LNCS, vol. 7473, pp. 500–507. Springer Heidelberg (2012)

    Google Scholar 

  16. Szathmary, L., Valtchev, P., and Napoli, A.: Generating Rare Association Rules Using the Minimal Rare Itemsets Family. International Journal on Software Informatics, 4, 219-238 (2010)

    Google Scholar 

  17. Yun, H., Ha, D., Hwang, B., Ryu, K.H.: Mining Association Rules on Significant Rare Data Using Relative Support. The Journal of Systems and Software, 67(3), 181-19 (2003)

    Google Scholar 

  18. Xiong, H., Shekhar, S., Tan, P-N., and Kumar, V.: Exploiting A Support-Based Upper Bond Pearson’s Correlation Coefficient for Efficiently Identifying Strongly Correlated Pairs. In The Proceeding of ACM SIGKDD’04, pp. 334-343 (2004)

    Google Scholar 

  19. Hoque, N., Nath, B., and Bhattacharyya, D.K.: A New Approach on Rare Association Rule Mining. International Journal of Computer Applications (0975 - 8887), vol. 53, no. 3, September 2012, 1-6 (2012)

    Google Scholar 

  20. Lavergne, J., Benton, R., and Raghavan, V.V.: TRARM-RelSup: Targeted Rare Association Rule Mining Using Itemset Trees and the Relative Support Measure. In L. Chen et al (Eds.): ISMIS 2012, LNAI, vol. 7661, pp. 61-70 (2012)

    Google Scholar 

  21. Rawat, S.S., and Rajamani, L.: Discovering Rare Association Rules using Probability Apriori in Grid Environments. In K.Deep et al. (Eds). Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011), AISC 131, pp. 527-539 (2012)

    Google Scholar 

  22. Zhou, L., and Yau, S.: Association Rule and Quantitative Association Rule Mining among Infrequent Items. In The Proceeding of ACM SIGKDD’07, pp. 15-32 (2007)

    Google Scholar 

  23. Ding, J.: Efficient Association Rule Mining among Infrequent Items. Ph.D Thesis, University of Illinois at Chicago, (2005)

    Google Scholar 

  24. Koh, Y.S., and Rountree, N.: Finding Sporadic Rules using Apriori-Inverse. LNCS, vol. 3518, pp. 97-106 (2005)

    Google Scholar 

  25. Liu, B., Hsu, W., and Ma, Y.: Mining Association Rules with Multiple Minimum Supports, SIGKDD Explorations, pp. 337 – 341 (1999)

    Google Scholar 

  26. Brin, S., Motwani, R., and Silverstein, C.: Beyond Market Baskets: Generalizing ARs to Correlations. Special Interest Group on Management of Data (SIGMOD’97), pp. 265-276 (1997)

    Google Scholar 

  27. Omniecinski, E.: Alternative Interest Measures for Mining Associations. IEEE Trans. Knowledge and Data Engineering, 15, 57-69 (2003)

    Google Scholar 

  28. Lee, Y.K., Kim, W.Y., Cai, Y.D., and Han, J.: CoMine: Efficient Mining of Correlated Patterns. The Proceeding of ICDM’03, pp.581-584 (2003)

    Google Scholar 

  29. Han, J., Pei, H., and Yin, Y.: Mining Frequent Patterns without Candidate Generation. The Proceeding of SIGMOD’00, ACM Press, pp 1-12 (2000)

    Google Scholar 

  30. Han, J., and Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, 2nd edition (2006)

    Google Scholar 

  31. Au, W.H., and Chan, K.C.C.: Mining Fuzzy ARs in a Bank-Account Database. IEEE Transactions on Fuzzy Systems 11 (2), 238-248 (2003)

    Google Scholar 

  32. Mangat, V.: Swarm Intelligence Based Technique for Rule Mining in the Medical Domain. International Journal of Computer Applications 4(1), 19-24 (2010)

    Google Scholar 

  33. Frequent Itemset Mining Dataset Repository, http://fimi.cs.helsinki.fi/data/

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Universiti Malaysia Terengganu, Kuala Terengganu, Malaysia

    Zailani Abdullah

  2. Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia

    Tutut Herawan

  3. Universitas Teknologi Yogyakarta, Yogyakarta, Indonesia

    Tutut Herawan

  4. Faculty of Science Computer and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia

    Mustafa Mat Deris

Authors
  1. Zailani Abdullah
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tutut Herawan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mustafa Mat Deris
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zailani Abdullah .

Editor information

Editors and Affiliations

  1. Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia

    Tutut Herawan

  2. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia

    Mustafa Mat Deris

  3. School of Information Technology, Deakin University, Burwood, Victoria, Australia

    Jemal Abawajy

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media Singapore

About this paper

Cite this paper

Abdullah, Z., Herawan, T., Mat Deris, M. (2014). Detecting Definite Least Association Rule in Medical Database. In: Herawan, T., Deris, M., Abawajy, J. (eds) Proceedings of the First International Conference on Advanced Data and Information Engineering (DaEng-2013). Lecture Notes in Electrical Engineering, vol 285. Springer, Singapore. https://doi.org/10.1007/978-981-4585-18-7_15

Download citation

  • DOI: https://doi.org/10.1007/978-981-4585-18-7_15

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-4585-17-0

  • Online ISBN: 978-981-4585-18-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation