Potential item set mining by using utility pattern growth model in big data

  • Authors

    • S. Angel Latha Mary
    • R. Divya
    • K. Uma Maheswari
    https://doi.org/10.14419/ijet.v7i1.3.9269

    Received date: January 24, 2018

    Accepted date: January 24, 2018

    Published date: December 31, 2017

  • High Utility Itemsets, UP-Tree, Utility Mining.

  • Abstract

    Information extracted by using data mining in earlier days. Now a day’s, the most talked about technology is Big Data. Utility Mining is the most crucial task in the real time application where the customers prefer to choose the item set which can yield more profit. Handling of large volume of transactional patterns becomes the complex issue in every application which is resolved in the existing work introducing the parallel utility mining process which will process the candidate item sets in the paralyzed manner by dividing the entire tasks into sub partition. Each sub partition would be processed in individual mapper and then be resulted with the final output value. The time complexity would be more when processing an unnecessary candidate item sets. This problem is resolved in the proposed methodology by introducing the novel approach called UP-Growth and UP-Growth+ which will prune the candidate item sets to reduce the dimension of the candidate item sets. The time complexity is further reduced by representing the candidate item sets in the tree layout. The test results prove that the proposed new approach provides better result than the existing work in terms of accuracy.

  • References

    1. Agrawal, R., and Srikant, R,“Fast algorithms for mining associ-ation rules”,20thVLDB Conference,pp. 203-208, 1994.
    2. Gaber, M., Zaslavsky, A., Krishnaswamy, S,“Mining data streams: a review”, ACMSigmod Record 34(2),pp. 18–26, 2005.
    3. Yao, H., Hamilton, H., Butz, C,“A foundational approach to mining itemset utilities from databases”,In: The 4th SIAM Inter-national Conference on Data Mining, pp. 482–486, 2004.
    4. Liu .Y., Liao, W.K., Choudhary, A, “A two-phase algorithm for fast discovery of high utility itemsets”, pp. 689–695, 2005.
    5. Hong, T.P., Lee, C.H., Wang, S.L,“Effective utility mining with the measure of average utility”, Expert Systems with Appli-cations 38,pp. 8259–8265, 2011.
    6. Lan, G.C., Hong, T.P., Tseng, V.S, “A Projection-Based Ap-proach for Discovering High Average-Utility Itemsets”, Journal of Information Science and Engineering 28, pp. 193–209.2012.
    7. Vo, B., Coenen, F., Le, B,“A new method for mining Frequent Weighted Itemsets basedon WIT-trees”, Expert Systems with Applications 40, pp. 1256–1264, 2013.
    8. Fournier-Viger, P., Wu, C.-W., Zida, S., Tseng, V.S, “FHM :Faster High-Utility Itemset Mining using Estimated Utility Co-occurrence Pruning”, In: Springer, ISMIS 2014. LNCS, vol. 8502,pp. 83–92, 2014.
    9. Tseng, V.S., Shie, B.-E., Wu, C.-W., Yu, P.S, “Efficient Algo-rithms for Mining High Utility Itemsets from Transactional Da-tabases”, IEEE Trans. Knowl. DataEng. 25(8),pp. 1772–1786, 2013.
    10. Yin, J., Zheng, Z., Cao, L., Song, Y., Wei, W,“Efficiently Min-ing Top-K High Utility Sequential Patterns”, In Proceedings of ICDM 2013, pp. 1259–1264, 2013.
    11. Baralis, E., Cerquitelli, T., &Chiusano, S.,“IMine: Index sup-port for item set mining”, IEEE TKDE Journal, 21(4), 493–506, 2009.
    12. Han, J., Cheng, H., Xin, D., & Yan, X.,“ Frequent itemset min-ing: Current status and future directions”, DMKD Journal, 15(1), pp .55–86, 2007.
    13. Angel Latha Mary.S, Clement King.A “Comparing and Identi-fying Common Factors in Frequent Item set algorithms in As-sociation Rule” in IEEE proceedings on Computing, Commu-nication and Networking, Publication Date: 18-20 Dec. 2008, On page(s): 1-5, ISBN: 978-1-4244-3594-4,Digital Object Identifier: 10.1109/ ICCCNET. 2008. 4787769, Current Ver-sion Published: 2009-02-24.
    14. Angel Latha Mary.S, Shankar Kumar.K.R “Study Experiments on Frequent Itemset Algorithms in Association Rule” Interna-tional Journal of Data Mining Techniques Volume: 01 No: 01 January 2012. pp34-41.

  • Downloads

  • How to Cite

    Angel Latha Mary, S., Divya, R., & Uma Maheswari, K. (2017). Potential item set mining by using utility pattern growth model in big data. International Journal of Engineering and Technology, 7(1.3), 66-68. https://doi.org/10.14419/ijet.v7i1.3.9269