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Enhanced Association Rule Mining Algorithm to Extract High Utility Itemsets from a Large Dataset
Abstract
Data mining aims at bringing out the hidden information from a large data set using data mining techniques according to the requirements. Association rule mining identifies itemsets that occur frequently in data set and frames association rules by taking all items equally. But many differences exist among the items that play a vital role in decision making. By taking one or more values of items as utilities, the utility mining technique works on finding the itemsets with greater utilities. In the proposed paper we present a utility mining algorithm named IUM (Improved Utility Mining) algorithm that finds high utility itemsets and also low utility itemsets from a large data set and the experiments states that the proposed algorithm performs better than existing algorithms in case of running time.
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References
Sadak Murali, Kolla Morarjee, A Survery on Efficient Algorithm for Mining High Utility Itemsets, IJREAT International Journal of Research in Engineering & Advanced Technology, Volume 1, Issue 5, Oct-Nov,2013.
Rachna Somkunwar, A study on various data mining Approaches of Association Rules, International Journal of Advanced Research in Computer Science and Software Engineering, Septermber 2012, pp, 141-144.
Ying Liu, Wei-kengLiao, Aloc Choudhary, A Two-Phase algorithm for Fast Discovery of High Utility Itemsets, PAKDD 2005, LNAI 3518, pp. 689 – 695, 2005. © Springer-Verlag Berlin Heidelberg 2005.
Mengchi Liu, Junfeng Qu, Mining High Utility Itemsets without Candidate Generation, CIKM’12, October 29–November 2, 2012, Maui, HI, USA.
P.Asha, Dr.T.Jebarajan, G.Saranya, A survey on Efficient Incremental Algorithm for Mining High Utility Itemsets in Distributed and Dynamic Database, International Journal of Emerging Technology and Advanced Engineering, Volume 4, Issue 2, January 2014.
Ying Liu, Wei-keng Liao, Alok Choudhary, A Fast High Utility Itemsets Mining Algorithm, UBDM ’05, August 21, 2005, Chicago, Illinois, USA.
Frequent Itemset Mining Dataset Repository. http:// fimi.ua.ac.be/, 2012.
Dr. T. Christopher, Character based Weighted Support threshold Algorithm using Multi criteria decision making technique, International Journal on Computer Science and Engineering, Vol. 02, No. 04, 2010, pp. 965-971.
Ms. Jyoti Ashokkumar Aidale, An Improved Up-Growth Algorithm for Mining High Utility Itemsets from Transactional Databases, International Journal of Scientific & Engineering Research, Volume 5, Issue 5, May 2014.
R. Agrawal and R. Srikant. Fast algorithms for mining association rules in large databases. In Proc. Int’l Conf. Very Large Data Bases, pages 487–499, 1994.
C. F. Ahmed, S. K. Tanbeer, B.-S. Jeong, and Y.-K. Lee. Efficient tree structures for high utility pattern mining in incremental databases. IEEE Transactions on Knowledge and Data Engineering, 21(12):1708–1721, 2009.
B. Barber and H. J. Hamilton. Extracting share frequent itemsets with infrequent subsets. Data Mining and Knowledge Discovery, 7(2):153–185, 2003.
A. Ceglar and J. F. Roddick. Association mining. ACM Computing Surveys, 38(2), 2006.
J. Han, H. Cheng, D. Xin, and X. Yan. Frequent pattern mining: Current status and future directions. Data Mining and Knowledge Discovery, 15(1):55–86, 2007.
“Data mining Concepts and Techniques” by By Jiawei Han, Micheline Kamber, Morgan Kaufmann Publishers, 2006.
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