Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

Efficient Algorithm For Mining High Utility Item Sets From Large Datasets Using Vertical Approach

Author : Dr.S.G.Sanjeevi 1 Aluguvelli Sindhu 2 Shrutika Pimpalkar 3 Sujith Srivardhan Arram 4

Date of Publication :7th April 2015

Abstract: High Utility Item set Mining is a challenging task as the Downward Closure Property present in frequent item set mining does not hold here. In recent times many algorithms have been proposed for mining high utility item set s ,but most of them follow a two-phase horizontal approach in which candidate item set s are generated first and then the actual high utility item set s are mined by performing another database scan. This approach generates a large number of candidate item set s which are not actual high utility item set s thus causing memory and time overhead to process them. To overcome this problem we propose a single phase algorithm which uses vertical database approach. Exhaustive search can mine all the high utility item set s but it is expensive and time consuming. Two strategies based on u-list structure and item pair co-existence map are used in this algorithm for efficiently pruning the search space to avoid exhaustive search. Experimental analysis over various databases show that the proposed algorithm outperforms the two-phase algorithms UP-Growth and other two phase algorithms in terms of running times and memory consumption.

Reference :

    1. R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In Proc. of the 20th Int'l Conf. on Very Large Data Bases, pp. 487-499, 1994.
    2. C. F. Ahmed, S. K. Tanbeer, B.-S. Jeong, and Y.-K. Lee. Efficient tree structures for high utility pattern mining in incremental databases. In IEEE Transactions on Knowledge and Data Engineering, Vol. 21, Issue 12, pp. 1708-1721, 2009.
    3. B.-E. Shie, V. S. Tseng, and P. S. Yu. Online mining of temporal maximal utility item set s from data streams. In Proc. of the 25th Annual ACM Symposium on Applied Computing, Switzerland, Mar., 2010..
    4. Vincent S. Tseng1 , Cheng-Wei Wu1 , Bai-En Shie1 , and Philip S. Yu2. UP-Growth: An Efficient Algorithm for High Utility Item set Mining, In IEEE Transactions on Knowledge and Data Engineering , Vol. 25, No. 8, August 2013.

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