Generating Frequent Patterns through Intersection between Transactions

The problem of frequent itemset mining is considered in this paper. One new technique proposed to generate frequent patterns in large databases without time-consuming candidate generation. This technique is based on focusing on transaction instead of concentrating on itemset. This algorithm based on take intersection between one transaction and others transaction and the maximum shared items between transactions computed instead of creating itemset and computing their frequency. With applying real life transactions and some consumption is taken from real life data, the significant efficiency acquire from databases in generation association rules mining.

Authors:

• M. Jamali
• F. Taghiyareh

Keywords:

• Association rules
• data mining
• frequent patterns
• shared itemset.

References:

[1]S. Brin, R. Motwani, J.D. Ullman, S. Tsur, "Dynamic Itemset Counting and
Implication Rules for Market Basket Data", SIGMOD Record, Volume 6,
Number 2: New York, June 1997, pp. 255 - 264.
[2]Su, Yibin, Dynamic Itemset Counting and Implication Rules for Market
Basket Data: Project Final Report, CS831, April 2000.
[3] H. Mannila and H. Toivonen. Levelwise search and borders of theories in
knowledge discovery. Data ining and Knowledge Discovery, 1(3):241- 258,
November 1997.
[4] D. Gunopulos, R. Khardon, H. Mannila, and H. Toivonen. Data mining,
hypergraph transversals, and machine learning. In Proceedings of the
Sixteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of
Database Systems, pages 209-216. ACM Press, 1997.
[5] H. Mannila. Global and local methods in data mining: basic techniques and
open problems. In P. Widmayer, F.T. Ruiz, R. Morales, M. Hennessy, S.
Eidenbenz, and R. Conejo, editors, Proceedings of the 29th International
Colloquium on Automata, Languages and Programming, volume 2380 of
Lecture Notes in Computer Science, pages 57-68. Springer, 2002.
[6]H. Toivonen. Sampling large databases for association rules. In T.M.
Vijayaraman, A.P. Buchmann, C. Mohan, and N.L. Sarda, editors,
Proceedings 22nd International Conference on Very Large Data Bases, pages
134-145. Morgan Kaufmann, 1996.
[7] J. Han, J. Pei, Y. Yin, and R. Mao. Mining frequent patterns without
candidate generation: A frequent-pattern tree approach. Data Mining and
Knowledge Discovery, 2003.
[8]Adamo,J.,Data mining for association rules and sequential
patterns,Springer,Berlin,2001
[9] R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, and A.I. Verkamo. Fast
discovery of association rules. In U.M. Fayyad, G. Piatetsky-Shapiro, P.
Smyth, and R. Uthurusamy, editors, Advances in Knowledge Discovery and
Data Mining, pages 307-328. MIT Press, 1996.
[10] T .Fukuda,Y. Morimoto, S.Moridhita, T. Tokuyama. Mining Optimized
Association Rules for Numeric Attributes. ACM PODS Conference
Proceedings, pages 182-191, 1996
[11] T. Fukuda, Y. Morimoto, S.Moridhita, T. Tokuyama. Data Mining using
Two-dimensional Optimized Association Rules for Numeric
Attributes:Schema, Algorithmd , Visualization. ACM SIGMOD Conference
Proceedings, pages 13-23, 1996
[12] R. Rastogi, K.Shim. Mining optimized Association rules for categorical
and numeric attributes. ICDE Conference Proceedings,pages 503-512,1998
[13] R. Rastogi, K.Shim. Mining optimized Support Rules for numeric
attributes. ICDE Conference Proceedings,pages 126-135,1999
[14] R.Srikant, R. Agrwal. Mining Generalized Association Rules. VLDB
Conference Proceedings, pages 407-419,1995
[15] R.Srikant, R. Agrwal. Mining Quantitative Association rules in large
databases. ACM SIGMOD Conference Proceedings, pages 1-12,1996