A New Rymon Tree Based Procedure for Mining Statistically Significant Frequent Itemsets

Authors

  • Predrag Stanisic University of Montenegro Department of Mathematics and Computer Science Dzordza Vasingtona bb, Podgorica, Montenegro
  • Savo Tomovic University of Montenegro Department of Mathematics and Computer Science Dzordza Vasingtona bb, Podgorica, Montenegro

Keywords:

frequent itemset mining, association analysis, Apriori algorithm, Rymon tree

Abstract

In this paper we suggest a new method for frequent itemsets mining, which is more efficient than well known Apriori algorithm. The method is based on special structure called Rymon tree. For its implementation, we suggest modified sort-merge-join algorithm. Finally, we explain how support measure, which is used in Apriori algorithm, gives statistically significant frequent itemsets.

References

Agrawal, R., Srikant, R., Fast Algorithms for Mining Association Rules, Proceedings of VLDB-94, 487-499, Santiago, Chile (1994)

Coenen, F.P., Leng, P., Ahmed, S., T-Trees, Vertical Partitioning and Distributed Association Rule Mining, Proceedings ICDM-2003, 513-516 (2003) http://dx.doi.org/10.1109/icdm.2003.1250965

Coenen, F.P., Leng, P., Ahmed, S., Data Structures for Association Rule Mining: T-trees and Ptrees, IEEE Transactions on Data and Knowledge Engineering, Vol. 16, No 6, 774-778 (2004) http://dx.doi.org/10.1109/TKDE.2004.8

Coenen, F.P., Leng, P., Goulbourne, G., Tree Structures for Mining Association Rules, Journal of Data Mining and Knowledge Discovery Vol. 8, No. 1, 25-51 (2004) http://dx.doi.org/10.1023/B:DAMI.0000005257.93780.3b

Goulbourne, G., Coenen, F., Leng, P., Algorithms for Computing Association Rules Using a Partial- Support Tree, Journal of Knowledge-Based Systems Vol. 13, 141-149 (1999) http://dx.doi.org/10.1016/S0950-7051(00)00055-1

Grahne, G., Zhu, J., Efficiently Using Prefix-trees in Mining Frequent Itemsets, Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations (2003)

Han, J., Pei, J., Yu, P.S., Mining Frequent Patterns without Candidate Generation, Proceedings of the ACM SIGMOD Conference on Management of Data, 1-12 (2000) http://dx.doi.org/10.1145/342009.335372

Rymon, R., Search Through Systematic Set Enumeration, Proceedings of 3rd International Conference on Principles of Knowledge Representation and Reasoning, 539-550 (1992)

Silberschatz, A., Korth, H. F., Sudarshan, S., Database System Concepts, Mc Graw Hill, New York (2006)

Simovici, A. D., Djeraba, C., Mathematical Tools for Data Mining (Set Theory, Partial Orders, Combinatorics), Springer-Verlag London Limited (2008)

Stanisic, P., Tomovic, S., Apriori Multiple Algorithm for Mining Association Rules, Information Technology and Control Vol. 37, No. 4, 311-320 (2008)

Tan., P.N., Steinbach, M., Kumar, V., Introduction to Data Mining, Addicon Wesley (2006).

Published

2010-11-01

Most read articles by the same author(s)

Obs.: This plugin requires at least one statistics/report plugin to be enabled. If your statistics plugins provide more than one metric then please also select a main metric on the admin's site settings page and/or on the journal manager's settings pages.