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Reseach Article

Mining High Utility Itemsets from Large Dynamic Dataset by Eliminating Unusual Items

by Switi C. Chaudhari, Vijay Kumar Verma
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 77 - Number 14
Year of Publication: 2013
Authors: Switi C. Chaudhari, Vijay Kumar Verma
10.5120/13550-1315

Switi C. Chaudhari, Vijay Kumar Verma . Mining High Utility Itemsets from Large Dynamic Dataset by Eliminating Unusual Items. International Journal of Computer Applications. 77, 14 ( September 2013), 12-18. DOI=10.5120/13550-1315

@article{ 10.5120/13550-1315,
author = { Switi C. Chaudhari, Vijay Kumar Verma },
title = { Mining High Utility Itemsets from Large Dynamic Dataset by Eliminating Unusual Items },
journal = { International Journal of Computer Applications },
issue_date = { September 2013 },
volume = { 77 },
number = { 14 },
month = { September },
year = { 2013 },
issn = { 0975-8887 },
pages = { 12-18 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume77/number14/13550-1315/ },
doi = { 10.5120/13550-1315 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:50:24.733491+05:30
%A Switi C. Chaudhari
%A Vijay Kumar Verma
%T Mining High Utility Itemsets from Large Dynamic Dataset by Eliminating Unusual Items
%J International Journal of Computer Applications
%@ 0975-8887
%V 77
%N 14
%P 12-18
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Utility-based data mining is a new research area interested in all types of utility factors in data mining processes [1]. The basic meaning of utility is the quantity sold, interest, importance & profitability of items to the users. Utility of items in a transaction database consists of two aspects: 1. The importance of distinct or unique items, which is called external utility. 2. The importance of the items in the transaction, w is called as internal utility. Mining high utility itemsets from the databases is not an easy task. Pruning search space for high utility itemset mining is difficult because a superset of a low utility itemset may be a high utility itemset. Existing studies [2,4,9] applied overestimated methods to facilitate the mining performance of utility mining. In these methods, first we will get potential high utility itemsets, and then an additional database scan is performed for identifying their utilities. However, the existing methods often generate a huge candidate itemsets and the mining performance is degraded consequently. In this paper we proposed Eliminating Unusual Itemset by Eliminating item set which is low utility item set to reduce search space. Proposed methods not only reduce the number of candidate itemsets, but also significantly increase the performance of the mining process.

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, Nov. , 2003.
  4. Y. L. Cheung, A. W. Fu, Mining frequent itemsets without support threshold: with and without item constraints. IEEE Transactions on Knowledge and Data Engineering, Vol. 16, No. 6, pp. 1052-1069, 2004.
  5. K. Chuang, J. Huang, M. Chen, Mining Top-K Frequent Patterns in the Presence of the Memory Constraint, The VLDB Journal, Vol. 17, pp. 1321-1344, 2008.
  6. A. Erwin, R. P. Gopalan and N. R. Achuthan. Efficient Mining of High-utility Itemsets from Large Datasets. In PAKDD 2008, LNAI 5012, pp. 554-561, 2008.
  7. A. W. Fu, R. W. Kwong and J. Tang, Mining N-Most Interesting Itemsets, In Proc. of ISMIS'00, 2000.
  8. J. Han, J. 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.
  9. J. Han, J. Wang, Y. Lu and P. Tzvetkov, "Mining Top-k Frequent Closed Patterns without Minimum Support," In Proc. of ICDM, 2002.
  10. Y. Hirate, E. Iwahashi and H. Yamana, TF2P-Growth: An Efficient Algorithm for Mining Frequent patterns without any Thresholds, In Proc. of ICDM 2004.
  11. H. -F. Li, H. -Y. Huang, Y. -C. Chen, Y. -J. Liu, S. -Y. Lee. Fast and Memory Efficient Mining of High Utility Itemsets in Data Streams. In Proc. of the 8th IEEE Int'l Conf. on Data Mining, pp. 881-886, 2008.
  12. Y. Liu, W. Liao, and A. Choudhary. A fast high-utility itemsets mining algorithm. In Proc. of the Utility-Based Data Mining Workshop, 2005.
  13. 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, 2008.
  14. S. Ngan, T. Lam, R. C. Wong and A. W. Fu, Mining N-most Interesting Itemsets without Support Threshold by the COFI-Tree, Int. J. Business Intelligence & Data Mining, Vol. 1, No. 1, pp. 88-106, 2005.
  15. J. Pisharath, Y. Liu, B. Ozisikyilmaz, R. Narayanan, W. K. Liao, A. Choudhary and G. Memik, NU-MineBench version 2. 0 dataset and technical report, http://cucis. ece. northwestern. edu/projects/DMS/MineBench. html
  16. T. M. Quang, S. Oyanagi, and K. Yamazaki, ExMiner: An Efficient Algorithm for Mining Top-K Frequent Patterns, ADMA 2006, LNAI 4093, pp. 436 – 447, 2006.
  17. L. Shen, H. Shen, P. Pritchard and R. Topor, Finding the N Largest Itemsets, in Proc. Int'l Conf. on Data Mining, pp. 211-222, 1998.
  18. B. -E. Shie, V. S. Tseng, and P. S. Yu. Online Mining of Temporal Maximal Utility Itemsets from Data Streams. In Proc. of the 25th Annual ACM Symposium on Applied Computing (ACM SAC 2010), 2010.
  19. V. 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 Int'l Conf. on ACM SIGKDD, pp. 253–262, 2010.
  20. V. S. Tseng, C. J. Chu, and T. Liang. Efficient mining of temporal high-utility itemsets from data streams. In ACM KDD Workshop on Utility-Based Data Mining Workshop, 2006.
  21. B. Vo, H. Nguyen, T. B. Ho, and B. Le. Parallel Method for Mining High-utility Itemsets from Vertically Partitioned Distributed Databases. In KES 2009, Part I, LNAI 5711, pp. 251-260, 2009.
  22. J. Wang and J. Han, TFP: An Efficient Algorithm for Mining Top-K Frequent Closed Itemsets, IEEE Transactions on Knowledge and Data Engineering, Vol. 17, No. 5, pp. 652-664, May 2005.
  23. 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, 2006.
  24. J. -S. Yeh, C. -Y. Chang and Y. -T. Wang. Efficient Algorithms for Incremental Utility Mining. In Proc. of the 2nd Int'l Conf. on Ubiquitous information management and communication, pp. 212-217, 2008.
  25. 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(DaWaK'2007), Lecture Notes in Computer Science (LNCS) 4654, pp. 283-292, 2007.
  26. S. Kannimuthu , Dr. K. Premalatha iFUM - Improved Fast Utility Mining International Journal of Computer Applications (0975 – 8887) Volume 27– No. 11, August 2011.
  27. Yao, H. , Hamilton, H. J. , Buzz, C. J. : A Foundational Approach to Mining Itemset Utilities from Databases. In: 4th SIAM International Conference on Data Mining. Florida USA (2004)
Index Terms

Computer Science
Information Sciences

Keywords

High Utility Mining Frequent Itemset Mining Eliminating Unusual Itemset Profit Quantity