International Journal of Intelligent Systems and Applications(IJISA)
ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)
Published By: MECS Press
IJISA Vol.6, No.11, Oct. 2014
Comparison of New Multilevel Association Rule Algorithm with MAFIA
Full Text (PDF, 356KB), PP.75-81
Multilevel association rules provide the more precise and specific information. Apriori algorithm is an established algorithm for finding association rules. Fast Apriori implementation is modified to develop new algorithm for finding frequent item sets and mining multilevel association rules. MAFIA is another established algorithm for finding frequent item sets. In this paper, the performance of this new algorithm is analyzed and compared with MAFIA algorithm.
Cite This Paper
Arpna Shrivastava, R. C. Jain, Ajay Kumar Shrivastava,"Comparison of New Multilevel Association Rule Algorithm with MAFIA", International Journal of Intelligent Systems and Applications(IJISA), vol.6, no.11, pp.75-81, 2014. DOI: 10.5815/ijisa.2014.11.10
R. Agrawal, T. Imielinski; A. Swami: Mining Association Rules Between Sets of Items in Large Databases", SIGMOD Conference 1993, pp. 207-216.
RAgrawal et al.(1994), Fast Algorithms for Mining Association Rules, Proceedings of the 20th International Conference on Very Large Data Bases, 1994, pp. 487-499.
J. Han, Y. Fu, “Discovery of Multiple-Level Association Rules from Large Database”, Proceeding of the 21st VLDB Conference Zurich, Swizerland, 1995, pp.420-431.
J. Han, Y. Fu, “Mining Multiple-Level Association Rules in Large Database”, IEEE transactions on knowledge & data engineering in 1999, pp.1-12.
B. Minaei-Bidgoli, R. Barmaki, M. Nasiri, “Mining numerical association rules via multi-objective genetic algorithms”, Information Sciences (233), Elsevier, 2013, pp.15–24.
M. Shaheen, M. Shahbaz, A. Guergachi, “Context based positive and negative spatio-temporal association rule mining”, Knowledge-Based Systems (37), Elsevier, 2013, pp. 261–273.
B. Chandra, S. Bhaskar, “A new approach for generating efficient sample from market basket data”, Expert Systems with Applications (38), Elsevier, 2011, pp. 1321–1325.
L. Xiang, “Simulation System of Car Crash Test in C-NCAP Analysis Based on an Improved Apriori Algorithm”, International Conference on Solid State Devices and Materials Science, Physics Procedia (25), Elsevier, 2012, pp. 2066 – 2071.
R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, and A. I. Verkamo. Fast discovery of association rules. In Advances in Knowledge Discovery and Data Mining, 1996, pp. 307.328.
Bing Liu,Wynne Hsu and Yiming Ma, “Mining association rules with multiple minimum supports”, ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 1999, pp.337-341.
F. Berzal, J. C. Cubero, Nicolas Marin, and Jose-Maria Serrano, “TBAR: An efficient method for association rule mining in relational databases”, Data and Knowledge Engineering 37, 2001, pp.47-64.
N. Rajkumar, M.R. Kartthik and S.N. Sivanandam, “Fast Algorithm for Mining Multilevel Association Rules”, Conference on Convergent Technologies for the Asia-Pacific Region, TENCON, 2003, pp.688-692.
Y. Li, “The Java Implementation of Apriori algorithm Based on Agile Design Principles”, 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT), 2010, pp. 329 – 331.
F. Bodon, “Fast Apriori Implementation”, Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations, 2003.
Doug Burdick, Manuel Calimlim and Johannes Gehrke, “MAFIA: A Maximal Frequent Itemset Algorithm for TransactionalDatabases” In Proceedings of the 17th International Conference on Data Engineering.Heidelberg, Germany, April 2001.