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

Authors

  • S. Angel Latha Mary
  • R. Divya
  • K. Uma Maheswari

DOI:

https://doi.org/10.14419/ijet.v7i1.3.9269

Keywords:

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 association rulesâ€,20thVLDB Conference,pp. 203-208, 1994.

[2] Gaber, M., Zaslavsky, A., Krishnaswamy, S,“Mining data streams: a reviewâ€, ACMSigmod Record 34(2),pp. 18–26, 2005.

[3] Yao, H., Hamilton, H., Butz, C,“A foundational approach to mining itemset utilities from databasesâ€,In: The 4th SIAM International 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 itemsetsâ€, pp. 689–695, 2005.

[5] Hong, T.P., Lee, C.H., Wang, S.L,“Effective utility mining with the measure of average utilityâ€, Expert Systems with Applications 38,pp. 8259–8265, 2011.

[6] Lan, G.C., Hong, T.P., Tseng, V.S, “A Projection-Based Approach for Discovering High Average-Utility Itemsetsâ€, 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-treesâ€, 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 Pruningâ€, 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 Algorithms for Mining High Utility Itemsets from Transactional Databasesâ€, IEEE Trans. Knowl. DataEng. 25(8),pp. 1772–1786, 2013.

[10] Yin, J., Zheng, Z., Cao, L., Song, Y., Wei, W,“Efficiently Mining Top-K High Utility Sequential Patternsâ€, In Proceedings of ICDM 2013, pp. 1259–1264, 2013.

[11] Baralis, E., Cerquitelli, T., &Chiusano, S.,“IMine: Index support for item set miningâ€, IEEE TKDE Journal, 21(4), 493–506, 2009.

[12] Han, J., Cheng, H., Xin, D., & Yan, X.,“ Frequent itemset mining: Current status and future directionsâ€, DMKD Journal, 15(1), pp .55–86, 2007.

[13] Angel Latha Mary.S, Clement King.A “Comparing and Identifying Common Factors in Frequent Item set algorithms in Association Rule†in IEEE proceedings on Computing, Communication 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 Version Published: 2009-02-24.

[14]Angel Latha Mary.S, Shankar Kumar.K.R “Study Experiments on Frequent Itemset Algorithms in Association Rule†International Journal of Data Mining Techniques Volume: 01 No: 01 January 2012. pp34-41.

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Published

2017-12-31