IPMA: Indirect Patterns Mining Algorithm

  • Tutut Herawan
  • A. Noraziah
  • Zailani Abdullah
  • Mustafa Mat Deris
  • Jemal H. Abawajy
Part of the Studies in Computational Intelligence book series (SCI, volume 457)

Abstract

Indirect pattern is considered as valuable and hidden information in transactional database. It represents the property of high dependencies between two items that are rarely occurred together but indirectly appeared via another items. Indirect pattern mining is very important because it can reveal a new knowledge in certain domain applications. Therefore, we propose an Indirect Pattern Mining Algorithm (IPMA) in an attempt to mine the indirect patterns from data repository. IPMA embeds with a measure called Critical Relative Support (CRS) measure rather than the common interesting measures. The result shows that IPMA is successful in generating the indirect patterns with the various threshold values.

Keywords

Indirect patterns Mining Algorithm Critical relative support 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From Data Mining to Knowledge Discovery in Databases, pp. 37–54 (1996)Google Scholar
  2. 2.
    Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules in Large Databases. In: Proceedings of the 20th International Conference on Very Large Data Bases, pp. 487–499 (1994)Google Scholar
  3. 3.
    Mannila, H., Toivonen, H., Verkamo, A.I.: Discovery of Frequent Episodes in Event Sequences. Data Mining and Knowledge Discovery 1, 259–289 (1997)CrossRefGoogle Scholar
  4. 4.
    Park, J.S., Chen, M.S., Yu, P.S.: An Effective Hash-based Algorithm for Mining Association Rules. In: Proceedings of the ACM-SIGMOD Int. Conf. Management of Data (SIGMOD 1995), pp. 175–186. ACM Press (1995)Google Scholar
  5. 5.
    Savasere, A., Omiecinski, E., Navathe, S.: An efficient algorithm for mining association rules in large databases. In: Proceedings of the 21st International Confenference on Very Large Data Bases (VLDB 1995), pp. 432–443. ACM Press (1995)Google Scholar
  6. 6.
    Fayyad, U., Patesesky-Shapiro, G., Smyth, P., Uthurusamy, R.: Advances in Knowledge Discovery and Data Mining. MIT Press, MA (1996)Google Scholar
  7. 7.
    Bayardo, R.J.: Efficiently Mining Long Patterns from Databases. In: Proceedings of the ACM-SIGMOD International Conference on Management of Data (SIGMOD 1998), pp. 85–93. ACM Press (1998)Google Scholar
  8. 8.
    Zaki, M.J., Hsiao, C.J.: CHARM: An efficient algorithm for closed itemset mining. In: Proceedings of the 2002 SIAM Int. Conf. Data Mining, pp. 457–473. SIAM (2002)Google Scholar
  9. 9.
    Agarwal, R., Aggarwal, C., Prasad, V.V.V.: A tree projection algorithm for generation of frequent itemsets. Journal of Parallel and Distributed Computing 61, 350–371 (2001)MATHCrossRefGoogle Scholar
  10. 10.
    Liu, B., Hsu, W., Ma, Y.: Mining Association Rules with Multiple Minimum Support. In: Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 337–341. ACM Press (1999)Google Scholar
  11. 11.
    Abdullah, Z., Herawan, T., Deris, M.M.: Scalable Model for Mining Critical Least Association Rules. In: Zhu, R., Zhang, Y., Liu, B., Liu, C. (eds.) ICICA 2010. LNCS, vol. 6377, pp. 509–516. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  12. 12.
    Abdullah, Z., Herawan, T., Deris, M.M.: Mining Significant Least Association Rules Using Fast SLP-Growth Algorithm. In: Kim, T.-h., Adeli, H. (eds.) AST/UCMA/ISA/ACN 2010. LNCS, vol. 6059, pp. 324–336. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  13. 13.
    Abdullah, Z., Herawan, T., Noraziah, A., Deris, M.M.: Extracting Highly Positive Association Rules from Students’ Enrollment Data. Procedia Social and Behavioral Sciences 28, 107–111 (2011)CrossRefGoogle Scholar
  14. 14.
    Abdullah, Z., Herawan, T., Noraziah, A., Deris, M.M.: Mining Significant Association Rules from Educational Data using Critical Relative Support Approach. Procedia Social and Behavioral Sciences 28, 97–101 (2011)CrossRefGoogle Scholar
  15. 15.
    Abdullah, Z., Herawan, T., Deris, M.M.: An Alternative Measure for Mining Weighted Least Association Rule and Its Framework. In: Zain, J.M., Wan Mohd, W.M.b., El-Qawasmeh, E. (eds.) ICSECS 2011, Part II. CCIS, vol. 180, pp. 480–494. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  16. 16.
    Abdullah, Z., Herawan, T., Deris, M.M.: Visualizing the Construction of Incremental Disorder Trie Itemset Data Structure (DOSTrieIT) for Frequent Pattern Tree (FP-Tree). In: Badioze Zaman, H., Robinson, P., Petrou, M., Olivier, P., Shih, T.K., Velastin, S., Nyström, I. (eds.) IVIC 2011, Part I. LNCS, vol. 7066, pp. 183–195. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  17. 17.
    Herawan, T., Yanto, I.T.R., Deris, M.M.: Soft Set Approach for Maximal Association Rules Mining. In: Ślęzak, D., Kim, T.-h., Zhang, Y., Ma, J., Chung, K.-i. (eds.) DTA 2009. CCIS, vol. 64, pp. 163–170. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  18. 18.
    Herawan, T., Yanto, I.T.R., Deris, M.M.: SMARViz: Soft Maximal Association Rules Visualization. In: Badioze Zaman, H., Robinson, P., Petrou, M., Olivier, P., Schröder, H., Shih, T.K. (eds.) IVIC 2009. LNCS, vol. 5857, pp. 664–674. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  19. 19.
    Herawan, T., Deris, M.M.: A soft set approach for association rules mining. Knowledge Based Systems 24(1), 186–195 (2011)CrossRefGoogle Scholar
  20. 20.
    Herawan, T., Vitasari, P., Abdullah, Z.: Mining Interesting Association Rules of Student Suffering Mathematics Anxiety. In: Zain, J.M., Wan Mohd, W.M.b., El-Qawasmeh, E. (eds.) ICSECS 2011, Part II. CCIS, vol. 180, pp. 495–508. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  21. 21.
    Abdullah, Z., Herawan, T., 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)CrossRefGoogle Scholar
  22. 22.
    Leung, C.W., Chan, S.C., Chung, F.: An Empirical Study of a Cross-level Association Rule Mining Approach to Cold-start Recommendations. Knowledge-Based Systems 21(7), 515–529 (2008)CrossRefGoogle Scholar
  23. 23.
    Tan, P.N., Kumar, V., Srivastava, J.: Indirect Association: Mining Higher Order Dependences in Data. In: Proceedings of the 4th European Conference on Principles and Practice of Knowledge Discovery in Databases, pp. 632–637. Springer, Heidelberg (2000)Google Scholar
  24. 24.
    Wan, Q., An, A.: An Efficient Approach to Mining Indirect Associations. Journal Intelligent Information Systems 27(2), 135–158 (2006)CrossRefGoogle Scholar
  25. 25.
    Kazienko, P.: Mining Indirect Association Rules for Web Recommendation. International Journal of Applied Mathematics and Computer Science 19(1), 165–186 (2009)MATHCrossRefGoogle Scholar
  26. 26.
    Tsuruoka, Y., Miwa, M., Hamamoto, K., Tsujii, J., Ananiadou, S.: Discovering and Visualizing Indirect Associations between Biomedical Concepts. Bioinformatics 27(13), 111–119 (2011)CrossRefGoogle Scholar
  27. 27.
    Chen, L., Bhowmick, S.S., Li, J.: Mining Temporal Indirect Associations. In: Ng, W.-K., Kitsuregawa, M., Li, J., Chang, K. (eds.) PAKDD 2006. LNCS (LNAI), vol. 3918, pp. 425–434. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  28. 28.
    Cornelis, C., Yan, P., Zhang, X., Chen, G.: Mining Positive and Negative Association from Large Databases. In: Proceedings of the 2006 IEEE International Conference on Cybernatics and Intelligent Systems, pp. 1–6. IEEE (2006)Google Scholar
  29. 29.
    Kazienko, P., Kuzminska, K.: The Influence of Indirect Association Rules on Recommendation Ranking Lists. In: Proceeding of the 5th International Conference on Intelligent Systems Design and Applications, pp. 482–487 (2005)Google Scholar
  30. 30.
    Tseng, V.S., Liu, Y.C., Shin, J.W.: Mining Gene Expression Data with Indirect Association Rules. In: Proceeding of the 2007 National Computer Symposium (2007)Google Scholar
  31. 31.
    Wu, X., Zhang, C., Zhang, S.: Efficient Mining of Positive and Negative Association Rules. ACM Transaction on Information Systems 22(3), 381–405 (2004)CrossRefGoogle Scholar
  32. 32.
    Abdullah, Z., Herawan, T., Noraziah, A., Deris, M.M.: Mining Significant Association Rules from Educational Data using Critical Relative Support Approach. Procedia Social and Behavioral Sciences 28, 97–191 (2011)CrossRefGoogle Scholar
  33. 33.
    Brin, S., Motwani, R., Ullman, J., Tsur, S.: Dynamic itemset counting and implication rules for market basket data. In: Proceedings of the International ACM SIGMOD Conference, pp. 255–264. ACM Press (1997)Google Scholar
  34. 34.
    Tan, P., Kumar, V., Srivastava, J.: Selecting the Right Interestingness Measure for Association Patterns. In: Proceedings of the 8th International Conference on Knowledge Discovery and Data Mining, pp. 32–41 (2002)Google Scholar
  35. 35.
    Lin, W.-Y., Wei, Y.-E., Chen, C.-H.: A Generic Approach for Mining Indirect Association Rules in Data Streams. In: Mehrotra, K.G., Mohan, C.K., Oh, J.C., Varshney, P.K., Ali, M. (eds.) IEA/AIE 2011, Part I. LNCS, vol. 6703, pp. 95–104. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  36. 36.
    UCI Machine Learning Repository: Car Evaluation Data Set, archive.ics.uci.edu/ml/datasets/

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Tutut Herawan
    • 1
  • A. Noraziah
    • 1
  • Zailani Abdullah
    • 2
  • Mustafa Mat Deris
    • 3
  • Jemal H. Abawajy
    • 4
  1. 1.Faculty of Computer System and Software EngineeringUniversiti Malaysia PahangPahangMalaysia
  2. 2.Department of Computer ScienceUniversiti Malaysia TerengganuTerengganuMalaysia
  3. 3.Faculty of Science Computer and Information TechnologyUniversiti Tun Hussein Onn MalaysiaJohorMalaysia
  4. 4.Scholl of Information TechnologyDeakin UniversityGeelongAustralia

Personalised recommendations