Cited By
View all- Singh KKumar RBiswas B(2021)High average-utility itemsets mining: a surveyApplied Intelligence10.1007/s10489-021-02611-zOnline publication date: 14-Jul-2021
Mining of High Average-Utility Itemsets with a Tighter Upper-Bound Model
Authors: 
Jerry Chun-Wei Lin, Shifeng Ren, 
Philippe Fournier-Viger, Tzung-Pei HongAuthors Info & Claims
Jerry Chun-Wei Lin, Shifeng Ren, Philippe Fournier-Viger, Tzung-Pei HongAuthors Info & ClaimsArticle No.: 37, Pages 1 - 6
Abstract
In this paper, we presented a tighter upper-bound model to instead of the traditional auub model for mining the HAUIs. A modified average-utility-list structure is also designed to keep the necessary information for later mining process, thus reducing the multiple database scans. Two pruning strategies are also respectively developed to reduce the search space for exploring the HAUIs compared with the state-of-the-art approach. Experiments showed that the designed algorithm with the new upper-bound model outperforms the traditional approach in terms of runtime and number of join operation.
References
[1]
R. Agrawal and R. Srikant. 1994. Fast algorithms for mining association rules. In International Conference on Very Large Data Bases. 487--499.
[2]
Philippe Fournier-Viger, Jerry Chun-Wei Lin, Antonio Gomariz, Ted Gueniche, Azadeh Soltani, and Zhihong Deng abd Hoang Thanh Lam. 2016. The SPMF open-source data mining library version 2 and beyond. In The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery. 36--40.
[3]
Philippe Fournier-Viger, Jerry Chun-Wei Lin, Rage Uday Kiran, Yun Sing Koh, and Rincy Thomas. 2017. A survey of sequential pattern mining. Data Science and Pattern Recognition 1, 1 (2017), 54--77.
[4]
Philippe Fournier-Viger, Cheng-Wei Wu, Souleymane Zida, and Vincent S. Tseng. 2014. FHM: faster high-utility itemset mining using estimated utility co-occurrence pruning. In Foundations of Intelligent Systems. 83--92.
[5]
Tzung-Pei Hong, Cho-Han Lee, and Shyue-Liang Wang. 2011. Effective utility mining with the measure of average utility. Expert System with Applacations 38, 7 (2011), 8259--8265.
[6]
Guo-Cheng Lan, Tzung-Pei Hong, and Vincent S. Tseng. 2012. Efficient mining high average-utility itemsets with an improved upper-bound strategy. International Journal of Information Technology & Decision Making 11, 5 (2012), 1009--1030.
[7]
Chun-Wei Lin, Tzung-Pei Hong, and Wen-Hsiang Lu. 2009. The Pre-FUFP algorithm for incremental mining. Expert Systems with Applications 36, 5 (2009), 9498--9505.
[8]
Chun-Wei Lin, Tzung-Pei Hong, and Wen-Hsiang Lu. 2010. Efficiently mining high average utility itemsets with a tree structure. In International Conference on Intelligent Information and Database Systems. 131--139.
[9]
Jerry Chun-Wei Lin, Wensheng Gan, Philippe Fournier-Viger, and Tzung-Pei Hong. 2016. FDHUP: Fast algorithm for mining discriminative high utility patterns. Knowledge and Information Systems (2016), 1--37.
[10]
Jerry Chun-Wei Lin, Wensheng Gan, Philippe Fournier-Viger, Tzung-Pei Hong, and Justin Zhan. 2016. Efficient mining of high-utility itemsets using multiple minimum utility thresholds. Knowledge-Based Systems 113 (2016), 100--115.
[11]
Jerry Chun-Wei Lin, Ting Li, Philippe Fournier-Viger, Tzung-Pei Hong, and JaHwung Su. 2016. Efficient mining of high average-utility itemsets with multiple minimum thresholds. In Industrial Conference on Data Mining. 14--28.
[12]
Jerry Chun-Wei Lin, Ting Li, Philippe Fournier-Viger, Tzung-Pei Hong, Justin Zhan, and Miroslav Voznak. 2016. An efficient algorithm to mine high average-utility itemsets. Advanced Engineering Informatics 30, 2 (2016), 233--243.
[13]
Mengchi Liu and Junfeng Qu. 2012. Mining high utility itemsets without candidate generation. In International Conference on Information and Knowledge Management. 55--64.
[14]
Ying Liu, Wei keng Liao, and Alok Choudhary. 2005. A fast high utility itemsets mining algorithm. In International Workshop on Utility-based Data Mining. 90--99.
[15]
Tien Lu, Bay Vo, Hien T. Nguyen, and Tzung-Pei Hong. 2014. A new method for mining high average utility itemsets. In Computer Information Systems and Industrial Management. 33--42.
[16]
H. Yao, H. J. Hamilton, and C. J. Butz. 2004. A foundational approach to mining itemset utilities from databases. In SIAM International Conference on Data Mining. 482--486.
[17]
Hong Yao, Howard J. Hamilton, and Cory J. Butz. 2004. A unified framework for utility based measures for mining itemsets. In International Workshop on Utility-Based Data Mining. 482--486.
[18]
Souleymane Zida, Philippe Fournier-Viger, Jerry Chun-Wei Lin, Cheng-Wei Wu, and Vincent S. Tseng. 2015. EFIM: a highly efficient algorithm for high-utility itemset mining. In Mexican International Conference on Artificial Intelligence. 530--546.
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- Mining of High Average-Utility Itemsets with a Tighter Upper-Bound Model
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July 2017
332 pages
ISBN:9781450348812
DOI:10.1145/3092090
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Published: 17 July 2017
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MISNC '17
MISNC '17: 4th Multidisciplinary International Social Networks Conference
July 17 - 19, 2017
Bangkok, Thailand
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Overall Acceptance Rate 57 of 97 submissions, 59%
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View all- Singh KKumar RBiswas B(2021)High average-utility itemsets mining: a surveyApplied Intelligence10.1007/s10489-021-02611-zOnline publication date: 14-Jul-2021
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