Mining top-K association rules

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7310 LNAI, Issue , 2012, Pages 61-73

  Fournier Viger, Philippe ^a   Wu, Cheng Wei ^b   Tseng, Vincent S ^b  

^a UNIVERSITÉ DE MONCTON   (Canada)

^b NATIONAL CHENG KUNG UNIVERSITY   (Taiwan)

Author keywords

association rule mining; rule expansion; support; top k rules

Indexed keywords

DATA MINING TASKS; EXCELLENT PERFORMANCE; FINE TUNING; MINIMUM SUPPORT; MINING ASSOCIATIONS; RULE MINING ALGORITHMS; TOP-K RULES;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; DATA MINING; SUPPORTS;

ASSOCIATION RULES;

EID: 84861752193     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-30353-1_6     Document Type: Conference Paper

References (13)

1. Agrawal, R., Imielminski, T., Swami, A.: Mining Association Rules Between Sets of Items in Large Databases. In: Proc. ACM Intern. Conf. on Management of Data, pp. 207-216. ACM Press (June 1993)
2. Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn. Morgan Kaufmann Publ., San Francisco (2006)
3. Han, J., Pei, J., Yin, Y., Mao, R.: Mining Frequent Patterns without Candidate Generation. Data Mining and Knowledge Discovery 8, 53-87 (2004)
4. Pei, J., Han, J., Lu, H., Nishio, S., Tang, S., Yang, D.: H-Mine: Fast and space-preserving frequent pattern mining in large databases. IIE Trans. 39(6), 593-605 (2007)
5. Webb, G.I., Zhang, S.: K-Optimal-Rule-Discovery. Data Mining and Knowledge Discovery 10(1), 39-79 (2005)
6. Webb, G.I.: Filtered top-k association discovery. WIREs Data Mining and Knowledge Discovery 1, 183-192 (2011)
7. Kun Ta, C., Huang, J.-L., Chen, M.-S.: Mining Top-k Frequent Patterns in the Presence of the Memory Constraint. VLDB Journal 17(5), 1321-1344 (2008)
8. Wang, J., Lu, Y., Tzvetkov, P.: Mining Top-k Frequent Closed Itemsets. IEEE Trans. Knowledge and Data Engineering 17(5), 652-664 (2005)
9. Pietracaprina, A., Vandin, F.: Efficient Incremental Mining of Top-K Frequent Closed Itemsets. In: Corruble, V., Takeda, M., Suzuki, E. (eds.) DS 2007. LNCS (LNAI), vol. 4755, pp. 275-280. Springer, Heidelberg (2007)
10. Tzvetkov, P., Yan, X., Han, J.: TSP: Mining Top-k Closed Sequential Patterns. Knowledge and Information Systems 7(4), 438-457 (2005)
11. You, Y., Zhang, J., Yang, Z., Liu, G.: Mining Top-k Fault Tolerant Association Rules by Redundant Pattern Disambiguation in Data Streams. In: Proc. 2010 Intern. Conf. Intelligent Computing and Cognitive Informatics, pp. 470-473. IEEE Press (March 2010)
12. Cormen, T.H., Leiserson, C.E., Rivest, R., Stein, C.: Introduction to Algorithms, 3rd edn. MIT Press, Cambridge (2009)
13. Lucchese, C., Orlando, S., Perego, R.: Fast and Memory Efficient Mining of Frequent Closed Itemsets. IEEE Trans. Knowl. and Data Eng. 18(1), 21-36 (2006)