Finding Efficient Positive and Negative Itemsets Using Interestingness Measures

  • Authors

    • P. Asha
    • T. Prem Jacob
    • A. Pravin
    2018-12-09
    https://doi.org/10.14419/ijet.v7i4.36.24133
  • Association rule, positive association rules, negative association ruls, Interestingness measures.

  • Currently, data gathering techniques have increased through which unstructured data creeps in, along with well defined data formats. Mining these data and bringing out useful patterns seems difficult. Various data mining algorithms were put forth for this purpose. The associated patterns generated by the association rule mining algorithms are large in number. Every ARM focuses on positive rule mining and very few literature has focussed on rare_itemsets_mining. The work aims at retrieving the rare itemsets that are of most interest to the user by utilizing various interestingness measures. Both positive and negative itemset mining would be focused in this work.

     

     

  • References

    1. [1] Agrawal R & Srikant R, “Fast algorithms for mining association rulesâ€, 20th int. conf. very large data bases, VLDB, Vol.1215, (1994), pp.487-499.

      [2] Simon GJ, Schrom J, Castro MR, Li PW & Caraballo PJ, “Survival association rule mining towards type 2 diabetes risk assessmentâ€, AMIA annual symposium proceedings, American Medical Informatics Association, (2013).

      [3] Sinduja K & Saravanan N, “Predicting Relative Risk for Diabetes Mellitus Using Association Rule Summarization Techniquesâ€, Imperial Journal of Interdisciplinary Research, Vol.2, No.6, (2016).

      [4] Srikant R, Vu Q & Agrawal R, “Mining association rules with item constraintsâ€, Kdd, Vol.97, (1997), pp.67-73.

      [5] Yin X & Han J, “CPAR: Classification based on predictive association rulesâ€, SIAM International Conference on Data Mining, 2003, pp.331-335.

      [6] Lin NP, Chen HJ, Hao WH, Chueh HE & Chang CI, “Mining strong positive and negative sequential patternsâ€, WSEAS Transactions on Computers, Vol.7, No.3, (2008), pp.119-124.

      [7] Bin Y, Xiangjun D & Fufu S, “Research of web usage mining based on negative association rulesâ€, IEEE International Forum on Computer Science-Technology and Applications, Vol.1, (2009), pp.196-199.

      [8] Pandian A & Thaveethu J, “SOTARM: Size of transaction-based association rule mining algorithmâ€, Turkish Journal of Electrical Engineering & Computer Sciences, Vol.25, No.1, (2017), pp.278-291.

      [9] Martin D, Rosete A, Alcala-Fdez J & Herrera F, “A new multiobjective evolutionary algorithm for mining a reduced set of interesting positive and negative quantitative association rulesâ€, IEEE Transactions on Evolutionary Computation, Vol.18, No.1, (2014), pp.54-69.

      [10] Asha P & Srinivasan S, “Analyzing the associations between infected genes using data mining techniquesâ€, International Journal of Data Mining and Bioinformatics, Inderscience Publishers, Vol.15, No.3, (2016), pp.250–271.

      [11] Ramakrishnudu T & Sbramanyam RBV, “Mining positive and negative association rules using fii-treeâ€, Editorial Preface, Vol.4, No.9, (2013).

      [12] Prem Jacob T & Ravi T, “An Optimal Technique for Reducing the Effort of Regression Testâ€, Indian Journal of Science and Technology, Vol.6, No.8, (2013), pp.5065-5069.

      [13] Soltani A & Akbarzadeh TMR, “Confabulation-inspired association rule mining for rare and frequent itemsetsâ€, IEEE Transactions on neural networks and learning systems, Vol.25, No.11, (2014), pp.2053-2064.

      [14] Asha P & Srinivasan S, “Distributed association rule mining with load balancing in grid environmentâ€, Journal of Computational and Theoretical Nanoscience, Vol.13, No.1, (2016), pp.33-42.

  • Downloads

  • How to Cite

    Asha, P., Prem Jacob, T., & Pravin, A. (2018). Finding Efficient Positive and Negative Itemsets Using Interestingness Measures. International Journal of Engineering & Technology, 7(4.36), 533-541. https://doi.org/10.14419/ijet.v7i4.36.24133