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High Utility Itemset Mining Using Transaction Utility of Itemsets
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 Title & Authors
High Utility Itemset Mining Using Transaction Utility of Itemsets
Lee, Serin; Park, Jong Soo;
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 Abstract
High utility itemset(HUI) mining refers to the discovery of itemsets with high utilities which are not less than a user-specified minimum utility threshold, by considering both the quantities and weight factors of items in a transaction database. Recently the utility-list based HUI mining algorithms have been proposed to avoid numerous candidate itemsets and the algorithms need the costly join operations. In this paper, we propose a new HUI mining algorithm, using the utility-list with additional attributes of transaction utility and common utility of itemsets. The new algorithm decreases the number of join operations and efficiently prunes the search space. Experimental results on both synthetic and real datasets show that the proposed algorithm outperforms other recent algorithms in runtime, especially when datasets are dense or contain many long transactions.
 Keywords
Data Mining;High Utility Pattern Mining;High Utility Itemsets;Transaction Utility;
 Language
Korean
 Cited by
 References
1.
R. Agrawal and R. Srikant, "Fast algorithms for mining association rules," in Proceedings of the 20th International Conference on Very Large Data Bases, Santiago, Vol.1215, pp.487-499, 1994.

2.
J. Han, J. Pei, and Y. Yin, "Mining frequent patterns without candidate generation," in Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, Dallas, pp.1-12, 2000.

3.
W. Wang, J. Yang, and P. Yu, "Efficient mining of weighted association rules (WAR)," in Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston, pp.270-274, 2000.

4.
F. Tao, F. Murtagh, and M. Farid, "Weighted association rule mining using weighted support and significance framework," in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, pp.661-666, 2003.

5.
H. Yao, H. J. Hamilton, and C. J. Butz, "A foundational approach to mining itemset utilities from databases," in Proceedings of the Fourth SIAM International Conference on Data Mining. SIAM, pp.482-486, 2004.

6.
Y. Liu, W. Liao, and A. Choudhary, "A fast high utility itemset mining algorithm," in Proceedings of the 1st International Workshop on Utility-Based Data Mining, ACM, Chicago, pp.90-99, 2005.

7.
Y. Liu, W. Liao, and A. Choudhary, "A two-phase algorithm for fast discovery of high utility itemsets," in Proceedings of the 9th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer, Vol.3518, pp.689-695, 2005.

8.
H. Yao and H. J. Hamilton, "Mining itemset utilities from transaction databases," Data & Knowledge Engineering, Elsevier, Vol.59, No.3, pp.603-626, 2006. crossref(new window)

9.
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, Vol.21, No.12, pp.1708-1721, 2009. crossref(new window)

10.
B.-S. Jeong, C. F. Ahmed, I. Lee, and H. Yong, "High utility pattern mining using a prefix-tree," Journal of KIISE: Database, Vol.36, No.5, pp.341-351, 2009. (in Korean)

11.
V. S. Tseng, C.-W. Wu, B.-E. Shie, and P. S. Yu, "UP-Growth: an efficient algorithm for high utility itemset mining," in Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, pp.253-262, 2010.

12.
V. S. Tseng, B.-E. Shie, C.-W. Wu, and P. S. Yu, "Efficient algorithms for mining high utility itemsets from transactional databases," IEEE Transactions on Knowledge and Data Engineering, Vol.25, No.8, pp.1772-1786, 2013. crossref(new window)

13.
M. Liu and J. Qu, "Mining high utility itemsets without candidate generation," in Proceedings of the 21st ACM International Conference on Information and Knowledge Management, Maui, pp.55-64, 2012.

14.
P. Fournier-Viger, C.-W. Wu, S. Zida, and V. S. Tseng, "FHM: Faster high-utility itemset mining using estimated utility co-occurrence pruning," Foundations of Intelligent Systems, Springer, pp.83-92, 2014.

15.
Frequent Itemset Mining Dataset Repository. Available at [Internet] http://fimi.cs.helsinki.fi/data/.