Research Article

An Improved UP-Growth High Utility Itemset Mining

by  Adinarayanareddy B, O. Srinivasa Rao, Mhm Krishna Prasad
journal cover
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 58 - Issue 2
Published: November 2012
Authors: Adinarayanareddy B, O. Srinivasa Rao, Mhm Krishna Prasad
10.5120/9255-3424
PDF

Adinarayanareddy B, O. Srinivasa Rao, Mhm Krishna Prasad . An Improved UP-Growth High Utility Itemset Mining. International Journal of Computer Applications. 58, 2 (November 2012), 25-28. DOI=10.5120/9255-3424

                        @article{ 10.5120/9255-3424,
                        author  = { Adinarayanareddy B,O. Srinivasa Rao,Mhm Krishna Prasad },
                        title   = { An Improved UP-Growth High Utility Itemset Mining },
                        journal = { International Journal of Computer Applications },
                        year    = { 2012 },
                        volume  = { 58 },
                        number  = { 2 },
                        pages   = { 25-28 },
                        doi     = { 10.5120/9255-3424 },
                        publisher = { Foundation of Computer Science (FCS), NY, USA }
                        }
                        %0 Journal Article
                        %D 2012
                        %A Adinarayanareddy B
                        %A O. Srinivasa Rao
                        %A Mhm Krishna Prasad
                        %T An Improved UP-Growth High Utility Itemset Mining%T 
                        %J International Journal of Computer Applications
                        %V 58
                        %N 2
                        %P 25-28
                        %R 10.5120/9255-3424
                        %I Foundation of Computer Science (FCS), NY, USA
Abstract

Efficient discovery of frequent itemsets in large datasets is a crucial task of data mining. In recent years, several approaches have been proposed for generating high utility patterns, they arise the problems of producing a large number of candidate itemsets for high utility itemsets and probably degrades mining performance in terms of speed and space. Recently proposed compact tree structure, viz. , UP-Tree, maintains the information of transactions and itemsets, facilitate the mining performance and avoid scanning original database repeatedly. In this paper, UP-Tree (Utility Pattern Tree) is adopted, which scans database only twice to obtain candidate items and manage them in an efficient data structured way. Applying UP-Tree to the UP-Growth takes more execution time for Phase II. Hence this paper presents modified algorithm aiming to reduce the execution time by effectively identifying high utility itemsets.

References
  • 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.
  • 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.
  • R. Chan, Q. Yang, and Y. Shen. : Mining high utility itemsets. In Proc. of Third IEEE Int'l Conf. on Data Mining, pp. 19-26, 2003.
  • A. Erwin, R. P. Gopalan, and N. R. Achuthan. : Efficient mining of high utility itemsets from large datasets. In Proc. of PAKDD 2008, LNAI 5012, pp. 554-561.
  • Jiawei. Han, Jian. Pei, and Y. Yin. : Mining frequent patterns without candidate generation. In Proc. of the ACM-SIGMOD Int'l Conf. on Management of Data, pp. 1-12, 2000.
  • Y. C. Li, J. S. Yeh, and C. C. Chang. : Isolated items discarding strategy for discovering high utility itemsets. In Data & Knowledge Engineering, Vol. 64, Issue 1, pp. 198-217, Jan. , 2008.
  • Y. Liu, W. Liao, and A. Choudhary. : A fast high utility itemsets mining algorithm. In Proc. of the Utility-Based Data Mining Workshop, 2005.
  • H. Yao, H. J. Hamilton, and 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.
  • S. J. Yen and Y. S. Lee. : Mining high utility quantitative association rules. In Proc. of 9th Int'l Conf. on Data Warehousing and Knowledge Discovery, Lecture Notes in Computer Science 4654, pp. 283-292, Sep. , 2007.
  • Frequent itemset mining implementations repository, http://fimi. cs. helsinki. fi/
  • Vincent. S. Tseng, C. W. Wu, B. E. Shie, and P. S. Yu. : UP-Growth: An Efficient Algorithm for High Utility Itemset Mining. In Proc. of ACM-KDD, Washington, DC, USA, pp. 253-262, July 25–28, 2010.
Index Terms
Computer Science
Information Sciences
No index terms available.
Keywords

High utility itemsets Transaction Weight Utilization Utility Mining Discarding

Powered by PhDFocusTM