A proposed Method for Mining High Utility Itemset with Transactional Weighted Utility using Genetic Algorithm Technique (MHUI_TWU-GA)

Pradeep K.Sharma¹ , Vaibhav Sharma² , Jagrati Nagdiya³

Section:Review Paper, Product Type: Isroset-Journal
Vol.5 , Issue.1 , pp.31-35, Feb-2017

Online published on Feb 28, 2017

Copyright © Pradeep K.Sharma, Vaibhav Sharma , Jagrati Nagdiya . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Utility mining is a technique to prune high utility itemset from the given transactional database on the basis of user-defined minimum utility threshold. Frequent itemset mining, only focus on itemset appear most frequently in the database while in utility mining we concern about utility i.e. importance or profit of itemset according to the user preference. In this paper we are proposing a two-phase algorithm, in the first phase, we are using weighted transaction utility concept to calculate and compare the utility of itemset with minimum utility threshold and then in the second phase, we are proposing genetic algorithm technique to search high utility itemset from the recognized transactional database obtain after the first phase.

Key-Words / Index Term :
Data Mining, Weighted Transaction Utility, Utility Mining, Genetic Algorithm

References :
[1]. S. Kannimuthu, Dr. K .Premalatha, Discovery of High Utility Itemsets Using Genetic Algorithm, International Journal of Engineering and Technology (IJET), Vol 5 No 6 Dec 2013-Jan 2014.
[2]. VincentS. Tseng, Cheng-Wei Wu, Bai-En Shie, and PhilipS.Yu.UP-Growth: An Efficient Algorithm for High Utility Itemset Mining. InKDD’10, July25–28, 2010, Washington, DC, USA.2010ACM.
[3]. Pradeepk. Sharma, Abhishe k Raghuvanshi, An Efficient Methodfor Mining High Utility Data fromaDataSet, in International Journal of Advanced Research in Computer Science and Software Engineering, Volume3, Issue11, November2013.
[4]. R.Agrawal and R.Srikant.Fast algorithms for mining association rules.InProc.ofthe20thInt`lConf.onVery Large Data Bases, pp.487-499, 1994.
[5]. J.Han,J.Pei,andY.Yin .Mining frequent patterns without candidate generation.InProc.of the ACM-SIGMOD Int`l Conf. on Management of Data, pp.1-12,2000.
[6]. H.Yao,H.J.Hamilton,L.Geng, A unified framework for utility-based measures for mining itemsets. In Proc.of ACM SIGKDD 2nd Workshop on Utility-Based Data Mining, pp.28-37, USA,Aug., 2006.
[7]. Y.Liu, W.Liao, and A.Choudhary.A fast high utility itemsets mining algorithm. InProc. ofthe Utility-Based Data Mining Workshop,2005.
[8]. Y.-C.Li,J.-S.Yeh,andC.-C.Chang.isolated items discarding strategy for discovering high utility itemsets, In Data &Knowledge Engineering, Vol. 64,Issue1, pp.198-217, Jan., 2008.
[9]. C.F.Ahmed,S.K.Tanbeer,B.-S.Jeong,andY.-K.Lee.Efficient tree structures for high utility pattern mining in incremental databases.In IEEE Transactionson Knowledge and Data Engineering,Vol.21,Issue12,pp.1708-1721,2009.
[10]. Yu-Chiang Li, Jieh-Shan Yeh and Chin-Chen Chang, "Isolated items discarding strategy for discovering high utility itemsets", Data and Knowledge Engineering, Elsevier Journal, Vol. 64, pp. 198-217, 2008.