Modified classic apriori algorithm for association rule mining

  • Authors

    • G Anitha
    • R A. Karthika
    • G Bindu
    • G V. Sriramakrishnan
    2018-04-20
    https://doi.org/10.14419/ijet.v7i2.21.12455
  • Apriori, frequent itemset, support, confidence, candidate itemset.

  • In today’s real world environment, information is the most critical element in all aspects of the life. It can be used to perform analysis and it helps to make decision making. But due to large collection of information the analysis and extraction of such useful information is tedious process which will create a major problem. In data mining, Association rules states about associations among the entities of known and unknown group and extracting hidden patterns in the data. Apriori algorithm is used for association rule mining. In this paper, due to limitations in rule condition, the algorithm was extended as new modified classic apriori algorithm which fulfills user stated minimum support and confidence constraints.

     

     

  • References

    1. [1] Han J & Kamber M, “Data Mining: Concepts and Techniquesâ€, Morgan Kaufmann Publishers, Book, (2000).

      [2] Aggarwal CC, Procopiuc CM & Yu PS, “Finding Localized Associations in Market Basket Dataâ€, Knowledge and Data Engineering, Vol.14, No.1, (2002), 51–62.

      [3] Borgelt C & Kruse R, “Induction of Association Rules: Apriori Implementationâ€, Proc. 15th Conf. on Computational Statistics (Compstat 2002, Berlin, Germany). Physika Verlag, Heidelberg, Germany, (2002).

      [4] Goethals B & Zaki MJ, “Advances in Frequent Itemset Mining Implementations: Report on FIMI’03â€, SIGKDD Explorations, Vol.6, No.1, (2004), pp.109–117.

      [5] Hipp J, G¨untzer U & Nakhaeizadeh G, “Algorithms for Association Rule Mining A General Survey and Comparisonâ€, SIGKDD Explorations, Vol.2, No.2, (2000), pp.1–58.

      [6] Rao S & Gupta R, “Implementing Improved Algorithm Over APRIORI Data Mining Association Rule Algorithmâ€, International Journal of Computer Science And Technology, (2012), pp.489-493.

      [7] Fayyad U, Piatetsky-Shapiro G & Smyth P, “From data mining to knowledge discovery in databases,†AI magazine, Vol.17, No.3, (1996), pp.1-37.

      [8] AL-Zawaida FH, Jbara YH & Marwan AL, “An improved algorithm for mining association rules in large databasesâ€, World of Computer Science and Information Technology Journal, Vol.1, No.7, (2011), pp.311-316.

      [9] Agrawal R, Imielinski T & Swami A, “Mining association rules between sets of items in large databasesâ€, ACM SIGMOD Record, Vol.22, (1993), pp.207–216.

      [10] Patel MR, Rana DP & Mehta RG, “FApriori: A modified Apriori algorithm based on checkpointâ€, IEEE International Conference on Information Systems and Computer Networks (ISCON), (2013), pp.50-53.

      [11] Lekha A, Srikrishna CV & Vinod V, “Utility of association rule mining: A case study using Weka toolâ€, IEEE International Conference on Emerging Trends in VLSI, Embedded System, Nano Electronics and Telecommunication System (ICEVENT), (2013), pp.1-6.

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  • How to Cite

    Anitha, G., A. Karthika, R., Bindu, G., & V. Sriramakrishnan, G. (2018). Modified classic apriori algorithm for association rule mining. International Journal of Engineering & Technology, 7(2.21), 414-416. https://doi.org/10.14419/ijet.v7i2.21.12455