A survey for acquiring frequent and sequential items in E-commerce sites

  • Authors

    • Haritha P
    • Sree Devi M
    • Ravali K
    • Manoj Pruthvi M
    2017-12-21
    https://doi.org/10.14419/ijet.v7i1.1.9484
  • Data Mining, Frequent Pattern Mining, Sequential Pattern Mining, Association Rule Mining, Sequence Rule Mining.

  • Large amounts of data has made available because of the increase in e-commerce industry. Data has high significance and also important for everyone. Hundreds of websites are being deployed and each site offers millions of products. In addition to this there are several types of input forms. Different sites have different input item collection. This means that there is a substantial amount of information being provided resulting in information overload and in turn results in reduced customer satisfaction and interest. This huge amount of data needs to get processed so that we can able to extract the useful information. From this useful information we can able to increase customer interest, satisfaction along with sales of e-commerce sites. Presenting frequent and sequential patterns in e-commerce sites results in increase of sales of products without delay. Different association rule mining techniques and sequential rule mining techniques can be used for different sets of input forms in order to generate frequent and sequential patterns. This paper discusses various algorithms using techniques such as association rule mining, sequence rule mining proposed for mining frequent and sequential items.

  • References

    1. [1] Z.A.Usmani,Shraddha Manchekar, Tahreem Malim, Ayman Mir, “A Predictive Approach for Improving the sales of Products in E-commerceâ€, 3rd International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics,2017.

      [2] M Sreedevi and L.S.S.Reddy†Mining Regular closed in trans-actional databasesâ€, IEEE Conference 2012 page No 380-383

      [3] M Sreedevi and L.S.S Reddy “Parallel and Distributed closed regular pattern mining in large databases†IJSCI.org, Volume 10 Issue 2 No 2 March 2013 Page No 264-269

      [4] Jun Yang, Haoxiang Huang, Xiaohui Jin,â€Mining Web Access Sequence with Improved Apriori Algorithmâ€, IEEE International Conference of Computational Science and Engineering (CSE), 2017.

      [5] Yeming Tang,Quili Tong, Zhao Du “ Mining frequent sequen-tial patterns and association rules on campus map systemâ€, 2nd International Conference on Systems and Informatics,2014.

      [6] M.Sreedevi and L.S.S.Reddy â€Closed Regular Pattern Mining using Vertical Format†IJSCET ,Volume 4 ,No 7 July 2013 Page no 1051-1056

      [7] Trupti A. Kumbhare, Santosh V.Chobe, â€An Overview of Association Rule mining Algorithmsâ€, International journal of computer science and information technologies,2014

      [8] Jia-Dong Ren, Yin-Bo Cheng, Liang-Liang Yang,†An Algo-rithm for Mining Generalized Sequential Patternsâ€, Proceedings of Third International Conference on Machine Learning and Cyber-netics,2004.

      [9] Peng Huang,†Improved algorithm based on Sequential Pattern Mining of Big Data Set “, IEEE, 2016. https://doi.org/10.1109/ICSESS.2016.7883028.

      [10] Mooney, C. H. and Roddick, J. F. 2013. Sequential pattern mining – Approaches and algorithms. ACM Comput. Surv. 45, 2, Article 19 (February 2013), 39 page.

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  • How to Cite

    P, H., Devi M, S., K, R., & Pruthvi M, M. (2017). A survey for acquiring frequent and sequential items in E-commerce sites. International Journal of Engineering & Technology, 7(1.1), 273-277. https://doi.org/10.14419/ijet.v7i1.1.9484