Statistical properties of transactional databases

Proceedings of the ACM Symposium on Applied Computing

Volumn 1, Issue , 2004, Pages 515-519

  Palmerini, P ^a   Orlando, S ^b   Perego, R ^a  

^a ISTI CNR   (Italy)

^b CA' FOSCARI UNIVERSITY OF VENICE   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; BENCHMARKING; COMPUTER SCIENCE; DATA MINING; DATA STRUCTURES; MATHEMATICAL MODELS; OPTIMIZATION; STATISTICAL MECHANICS;

BITMAP REPRESENTATION; DATASET COMPLEXITY; FREQUENT SET COUNTING (FSC); IDENTITY MATRIX;

DATABASE SYSTEMS;

EID: 2442589760     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/967900.968009     Document Type: Conference Paper

References (12)

1. Rakesh Agrawal and Ramakrishnan Srikant. Fast Algorithms for Mining Association Rules. In Proc. of the 20th Int'l Conference on Very Large Databases, Santiago, Chile, September 1994.
2. Bin Chen, Peter Haas, and Peter Scheuermann. A new two-phase sampling based algorithm for discovering association rules, In Proceedings ACM-SIGKDD Conference, Edmonton, Canada, July 2002.
3. F. Geerts, B. Goethals, and J. Van den Bussche. A tight upper bound on the number of candidate patterns. In N. Cercone, T.Y. Lin, and X. Wu, editors, Proceedings of the 2001 IEEE International Conference on Data Mining, pages 155-162. IEEE Computer Society, 2001.
4. Bart Goethals. Efficient Frequent Itemset Mining. PhD thesis, Limburg University, Belgium, 2003.
5. J. Han, J. Pei, and Y. Yin. Mining Frequent Patterns without Candidate Generation. In Proc. of the ACM SIGMOD Int. Conf. on Management of Data, pages 1-12, Dallas, Texas, USA, 2000.
6. Hipp, J. and Güntzer, U. and Nakhaeizadeh, G. Algorithms for Association Rule Mining - A General Survey and Comparison. SIGKDD Explorations, 2(1):58-64, June 2000.
7. J. Liu, Y. Pan, K. Wang, and J. Han. Mining Frequent Item Sets by Opportunistic Projection. In Proc. 2002 Int. Conf. on Knowledge Discovery in Databases (KDD'02), Edmonton, Canada, 2002.
8. S. Orlando, P. Palmerini, R. Perego, and F. Silvestri. Adaptive and resource-awere, mining of frequent sets. In Proceedings of IEEE International Conference on Data Mining, 2002.
9. J. S. Park, M.-S. Chen, and P. S. Yu. An-Effective Hash Based Algorithm for Mining Association Rules. In Proc. of the 1995 ACM SIGMOD Int. Conf. on Management of Data, pages 175-186, 1995.
10. G. Ramesh, W. A. Maniatty, and M. J. Zaki. Feasible itemset distributions in data mining: Theory and application. In 22nd ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, June 2003.
11. M. J. Zaki and K. Gouda. Fast Vertical Mining Using Diffsets, In 9th Int. Conf. on Knowledge Discovery and Data Mining, Washington, DC, 2003.
12. M. J. Zaki, S. Parthasarathy, W. Li, and Ogihara M. Evaluation of sampling for data mining of association rules. In 7th International Workshop on Research Issues in Data Engineering.