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An Improved UP-Growth High Utility Itemset Mining

by Adinarayanareddy B, O. Srinivasa Rao, Mhm Krishna Prasad
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 58 - Number 2
Year of Publication: 2012
Authors: Adinarayanareddy B, O. Srinivasa Rao, Mhm Krishna Prasad
10.5120/9255-3424

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 },
issue_date = { November 2012 },
volume = { 58 },
number = { 2 },
month = { November },
year = { 2012 },
issn = { 0975-8887 },
pages = { 25-28 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume58/number2/9255-3424/ },
doi = { 10.5120/9255-3424 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:01:31.048347+05:30
%A Adinarayanareddy B
%A O. Srinivasa Rao
%A Mhm Krishna Prasad
%T An Improved UP-Growth High Utility Itemset Mining
%J International Journal of Computer Applications
%@ 0975-8887
%V 58
%N 2
%P 25-28
%D 2012
%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
  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. 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.
  4. 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.
  5. 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.
  6. 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.
  7. Y. Liu, W. Liao, and A. Choudhary. : A fast high utility itemsets mining algorithm. In Proc. of the Utility-Based Data Mining Workshop, 2005.
  8. 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.
  9. 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.
  10. Frequent itemset mining implementations repository, http://fimi. cs. helsinki. fi/
  11. 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

Keywords

High utility itemsets Transaction Weight Utilization Utility Mining Discarding

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