Metamorphosis: Mining maximal frequent sets in dense domains

International Journal on Artificial Intelligence Tools

Volumn 14, Issue 3, 2005, Pages 491-505

  Srikumar, Krishnamoorthy ^a   Bhasker, Bharat ^b  

^a INFOSYS TECHNOLOGIES LTD   (India)

^b INDIAN INSTITUTE OF MANAGEMENT   (India)

Author keywords

Association rule mining; Maximal frequent sets; Maximum collapsible and compressible format

Indexed keywords

EID: 33746242395     PISSN: 02182130     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0218213005002223     Document Type: Article

References (14)

1. R. Agrawal, T. Imielinski, and A. Swami, Mining association rules between sets of items in large databases, In proceedings of ACM SIGMOD Conference on Management of Data, (1993) 207-216
2. J. Han, and M. Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers, San Francisco, CA (2001)
3. RJ. Bayardo, Brute-Force Mining of High-Confidence Classification Rules, In Proc. of the Third Int'l Conf. On Knowledge Discovery and Data Mining, (1997) 123-126
4. S. Brin, R. Motwani, J. Ullman, and S. Tsur, Dynamic Itemset Counting and Implication Rules for Market Basket Data, In Proc. of the 1997 SIGMOD Conf. On the Management of Data, (1997) 255-264
5. D. I. Lin, Z.M. Kedem, Pincer Search: A new algorithm for discovering the maximum frequent set. Intl Conf. on Extending database technology (1998)
6. R. J. Bayardo, Efficiently mining long patterns from databases, ACM SIGMOD conference on management of data (1998)
7. M. J. Zaki, and K. Gouda, Fast vertical mining using diffsets, RPI Technical Report, 01-1 (2001)
8. D. Gunopulos, H. Mannila, and S. Saluja, Discovering all the most specific sentences by randomized algorithms, In Intl. Conf. On Database Theory (1997)
9. R. Agrawal, C. Aggarwal, and V.V.V. Prasad, Depth first generation of Long patterns, 7th International conference on Knowledge discovery and Data mining (2000)
10. D. Burdick, M. Calimlim, and J. Gehrke, MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases, In Intl. Conf. On Data Engineering (2001)
11. K. Srikumar, and B. Bhasker, An Algorithm for Mining Maximal Frequent Sets based on Dominancy of Transactions, In Proc. of Intl. Conf. on Enterprise Information Systems, Angers, France (2003)
12. R. Agrawal et al, The Quest Data Mining System, Technical report, IBM Almaden Research Center, (1996b) Retrieved from http://www.almaden.ibm.com/cs/ quest/ in October 2002.
13. st Indian International Conference on Artificial Intelligence, Hyderabad, India (2003)
14. K. Srikumar, and B. Bhasker, Efficiently Mining Maximal Frequent Sets for Discovering Association Rules, In Proc. of Intl. Database Engineering and Applications Symposium (IDEAS '03), Hong Kong (2003)