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DFP-Growth: An Efficient Algorithm for Mining Frequent Patterns in Dynamic Database

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNISA,volume 7473)

Abstract

Mining frequent patterns in a large database is still an important and relevant topic in data mining. Nowadays, FP-Growth is one of the famous and benchmarked algorithms to mine the frequent patterns from FP-Tree data structure. However, the major drawback in FP-Growth is, the FP-Tree must be rebuilt all over again once the original database is changed. Therefore, in this paper we introduce an efficient algorithm called Dynamic Frequent Pattern Growth (DFP-Growth) to mine the frequent patterns from dynamic database. Experiments with three UCI datasets show that the DFP-Growth is up to 1.4 times faster than benchmarked FP-Growth, thus verify it efficiencies.

Keywords

  • Efficient algorithm
  • Frequent patterns
  • Dynamic database

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References

  1. 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)

    CrossRef  Google Scholar 

  2. Pei, J., Han, J., Lu, H., Nishio, S., Tang, S., Yang, D.: Hmine: Hyper-Structure Mining of Frequent Patterns in Large Databases. In: Proceedings of IEEE International Conference on Data Mining, pp. 441–448 (2001)

    Google Scholar 

  3. Pietracaprina, A., Zandolin, D.: Mining Frequent Item sets Using Patricia Tries. In: IEEE ICDM 2003, Workshop on Frequent Itemset Mining Implementations, pp. 3–14 (2003)

    Google Scholar 

  4. Grahne, G., Zhu, J.: Efficiently using Prefix-Trees in Mining Frequent Itemsets. In: Proceeding of Workshop Frequent Itemset Mining Implementations, pp. 123–132 (2003)

    Google Scholar 

  5. Han, J., Pei, H., Yin, Y.: Mining Frequent Patterns without Candidate Generation. In: Proceeding of the 2000 ACM SIGMOD, pp. 1–12 (2000)

    Google Scholar 

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

    CrossRef  Google Scholar 

  7. Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: Proceeding of 20th VLDB Conference, pp. 487–499. Morgan Kaufmann, Santiago (1994)

    Google Scholar 

  8. Brin, S., Motwani, R., Ullman, J.D., Tsur, S.: Dynamic Itemset Counting and Implication Rules for Market Basket Data. In: Proc. ACM SIGMOD, International Conference on Management of Data, pp. 255–264. ACM Press, New York (1997)

    CrossRef  Google Scholar 

  9. Park, J.S., Chen, M., Yu, P.S.: An Effective Hash Based Algorithm for Mining Association Rules. In: International Conference Management of Data, ACM SIGMOD, vol. 24(2), pp. 175–186. ACM, San Jose (1995)

    Google Scholar 

  10. Hipp, J., Guntzer, U., Nakhaeizadeh, G.: Algorithms for Association Rule Mining – A General Survey and Comparison. In: The Proceedings of SIGKDD Explorations. ACM SIGKDD, vol. 2(1), pp. 58–64. ACM, New York (2000)

    Google Scholar 

  11. Ji, L., Zhang, B., Li, J.: A New Improvement of Apriori Algorithm. In: Proceeding of International Conference on Computer Intelligence and Security 2006, pp. 840–844. Springer, Guangzhou (2006)

    CrossRef  Google Scholar 

  12. Anad, R., Agrawal, R., Dhar, J.: Variable Support Based Association Rules Mining. In: Proceeding of the 33rd Annual IEEE International Computer Software and Application Conference, pp. 25–30. EEE Computer Society, Washington (2009)

    CrossRef  Google Scholar 

  13. Herawan, T., Vitasari, P., Abdullah, Z.: Mining Interesting Association Rules on Student Suffering Study Anxieties using SLP-Growth Algorithm. International Journal of Knowledge and Systems Science 3(2), 24–41 (2012)

    CrossRef  Google Scholar 

  14. 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)

    CrossRef  Google Scholar 

  15. 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)

    CrossRef  Google Scholar 

  16. 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)

    CrossRef  Google Scholar 

  17. 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)

    CrossRef  Google Scholar 

  18. 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)

    CrossRef  Google Scholar 

  19. 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)

    CrossRef  Google Scholar 

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

    CrossRef  Google Scholar 

  21. 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)

    CrossRef  Google Scholar 

  22. 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)

    CrossRef  Google Scholar 

  23. Frequent Itemset Mining Dataset Repository, http://fimi.ua.ac.be/data/

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Abdullah, Z., Herawan, T., Noraziah, A., Deris, M.M. (2012). DFP-Growth: An Efficient Algorithm for Mining Frequent Patterns in Dynamic Database. In: Liu, B., Ma, M., Chang, J. (eds) Information Computing and Applications. ICICA 2012. Lecture Notes in Computer Science, vol 7473. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34062-8_7

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  • DOI: https://doi.org/10.1007/978-3-642-34062-8_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34061-1

  • Online ISBN: 978-3-642-34062-8

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