KHẢO SÁT CÁC PHƯƠNG PHÁP KHAI THÁC ITEMSET TRÊN CƠ SỞ DỮ LIỆU SỐ LƯỢNG
Abstract
Báo cáo trình bày tổng quan các nghiên cứu gần đây trong việc khai thác trên cơ sở dữ liệu số lượng (quantitative databases) như khai thác FWI (Frequent Weighted Itemsets), khai thác FWUI (Frequent Weighted Utility Itemsets), HUI (High Utility Itemsets) và trình bày một số hướng mở của bài toán khai thác trên cơ sở dữ liệu số lượngReferences
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[2] A. Erwin, R. P. Gopalan, N. R. Achuthan. A bottom-up projection based algorithm for mining high utility itemsets. Proceedings of the 2nd international workshop on Integrating artificial intelligence and data mining, Gold Coast, Australia, 3 – 11 (2007).
[3] A. Erwin, R. P. Gopalan, N. R. Achuthan. CTU-Mine: An efficient high utility itemset mining algorithm using the pattern growth approach. Proceedings of the IEEE 7th International Conferences on Computer and Information Technology, Aizu Wakamatsu, Japan, 71 – 76 (2007).
[4] M. S. Khan, M. K. Muyeba, F. Coenen. A weighted utility framework for mining association rules. Proceedings of Second UKSIM European Symposium on Computer Modeling and Simulation Second UKSIM European Symposium on Computer Modeling and Simulation, 87 – 92 (2008).
[5] B. Le, H. Nguyen, T.A. Cao, B. Vo. A novel algorithm for mining high utility itemsets. The 1st Asian Conference on Intelligent Information and Database Systems, pp.13–17 (2009).
[6] B. Le, H. Nguyen, B. Vo. An efficient strategy for mining high utility itemsets. Int. J. Intelligent Information and Database Systems, 5(2), pp.164–176 (2011).
[7] Y.C. Li, J.S. Yeh, C.C.Chang. Isolated items discarding strategy for discovering high utility itemsets. Data & Knowledge Engineering, 64(1), pp.198–217 (2008).
[8] Y. Liu, W. Liao, A. Choudhary. A fast high utility itemsets mining algorithm. UBDM ‘05, 21 August, Chicago, Illinois, USA, pp.90–99 (2005).
[9] M. K. Muyeba, M. S. Khan, F. Coenen. Fuzzy weighted association rule mining with weighted support and confidence framework. Proceedings of 1st Int Workshop on Algorithms for Large-Scale Information Processing in Knowledge Discovery (ALSIP 2008), held in conjunction with PAKDD 2008 (Japan), 52-64 (2008).
[10] G. D. Ramkumar, R. Sanjay, S. Tsur. Weighted association rules: Model and algorithm. Proceedings of SIGKDD’98, New York, USA, 661-666 (1998).
[11] F. Tao, F. Murtagh, M. Farid. Weighted association rule mining using weighted support and significance framework. Proceeedings of SIGKDD’03, Washington DC, USA, 661-666 (2003).
[12] W. Wang, J. Yang, P. S. Yu. Efficient mining of weighted association rules. Proceedings of SIGKDD’00, Boston, MA, USA, 270-274 (2000).
[13] H. Yao, H. J. Hamilton, C. J. Butz. A foundational approach to mining itemset utilities from databases. Proceedings SIAM International Conference on Data Mining, Florida, USA, 482-486 (2004).
[14] H. Yao, H. J. Hamilton. Mining itemsets utilities from transaction databases. Data and Knowledge Engineering 59(3), 603-626 (2005).
[15] H. Yao, H. J. Hamilton, L. Geng. A unified framework for utility based measures for mining itemsets. Proceedings of UBDM'06, Pennsylvania, USA, 28-37 (2006).
[16] B. Zadrozny, G. M. Weiss, M. Saar-Tsechansky. UBDM 2006: Utility-Based data mining 2006 workshop report. ACM SIGKDD Explorations 8(2), 98-101 (2006).
[17] B. Vo, F. Coenen, B. Le. A New Method for Mining Frequent Weighted Itemsets Based on WIT-trees. Expert Systems with Applications 40(4), 1256-1264(2013).
[18] B. Vo, B. Le, J. J. Jung. A tree-based approach for mining frequent weighted utility itemsets. ICCCI 2012, LNAI Vol. 7653 (Springer), 114-123 (2012).
[2] A. Erwin, R. P. Gopalan, N. R. Achuthan. A bottom-up projection based algorithm for mining high utility itemsets. Proceedings of the 2nd international workshop on Integrating artificial intelligence and data mining, Gold Coast, Australia, 3 – 11 (2007).
[3] A. Erwin, R. P. Gopalan, N. R. Achuthan. CTU-Mine: An efficient high utility itemset mining algorithm using the pattern growth approach. Proceedings of the IEEE 7th International Conferences on Computer and Information Technology, Aizu Wakamatsu, Japan, 71 – 76 (2007).
[4] M. S. Khan, M. K. Muyeba, F. Coenen. A weighted utility framework for mining association rules. Proceedings of Second UKSIM European Symposium on Computer Modeling and Simulation Second UKSIM European Symposium on Computer Modeling and Simulation, 87 – 92 (2008).
[5] B. Le, H. Nguyen, T.A. Cao, B. Vo. A novel algorithm for mining high utility itemsets. The 1st Asian Conference on Intelligent Information and Database Systems, pp.13–17 (2009).
[6] B. Le, H. Nguyen, B. Vo. An efficient strategy for mining high utility itemsets. Int. J. Intelligent Information and Database Systems, 5(2), pp.164–176 (2011).
[7] Y.C. Li, J.S. Yeh, C.C.Chang. Isolated items discarding strategy for discovering high utility itemsets. Data & Knowledge Engineering, 64(1), pp.198–217 (2008).
[8] Y. Liu, W. Liao, A. Choudhary. A fast high utility itemsets mining algorithm. UBDM ‘05, 21 August, Chicago, Illinois, USA, pp.90–99 (2005).
[9] M. K. Muyeba, M. S. Khan, F. Coenen. Fuzzy weighted association rule mining with weighted support and confidence framework. Proceedings of 1st Int Workshop on Algorithms for Large-Scale Information Processing in Knowledge Discovery (ALSIP 2008), held in conjunction with PAKDD 2008 (Japan), 52-64 (2008).
[10] G. D. Ramkumar, R. Sanjay, S. Tsur. Weighted association rules: Model and algorithm. Proceedings of SIGKDD’98, New York, USA, 661-666 (1998).
[11] F. Tao, F. Murtagh, M. Farid. Weighted association rule mining using weighted support and significance framework. Proceeedings of SIGKDD’03, Washington DC, USA, 661-666 (2003).
[12] W. Wang, J. Yang, P. S. Yu. Efficient mining of weighted association rules. Proceedings of SIGKDD’00, Boston, MA, USA, 270-274 (2000).
[13] H. Yao, H. J. Hamilton, C. J. Butz. A foundational approach to mining itemset utilities from databases. Proceedings SIAM International Conference on Data Mining, Florida, USA, 482-486 (2004).
[14] H. Yao, H. J. Hamilton. Mining itemsets utilities from transaction databases. Data and Knowledge Engineering 59(3), 603-626 (2005).
[15] H. Yao, H. J. Hamilton, L. Geng. A unified framework for utility based measures for mining itemsets. Proceedings of UBDM'06, Pennsylvania, USA, 28-37 (2006).
[16] B. Zadrozny, G. M. Weiss, M. Saar-Tsechansky. UBDM 2006: Utility-Based data mining 2006 workshop report. ACM SIGKDD Explorations 8(2), 98-101 (2006).
[17] B. Vo, F. Coenen, B. Le. A New Method for Mining Frequent Weighted Itemsets Based on WIT-trees. Expert Systems with Applications 40(4), 1256-1264(2013).
[18] B. Vo, B. Le, J. J. Jung. A tree-based approach for mining frequent weighted utility itemsets. ICCCI 2012, LNAI Vol. 7653 (Springer), 114-123 (2012).
Published
2014-11-26
How to Cite
BẢY, Võ Đình.
KHẢO SÁT CÁC PHƯƠNG PHÁP KHAI THÁC ITEMSET TRÊN CƠ SỞ DỮ LIỆU SỐ LƯỢNG.
JBIS, [S.l.], nov. 2014.
Available at: <http://jbis.ueh.edu.vn/index.php/TSTHQL/article/view/21>. Date accessed: 06 may 2024.
Issue
Section
Bài viết
Keywords
cơ sở dữ liệu số lượng
