Call for Paper - November 2022 Edition
IJCA solicits original research papers for the November 2022 Edition. Last date of manuscript submission is October 20, 2022. Read More

A Novel Algorithm for Cross Level Frequent Pattern Mining in Multidatasets

Print
PDF
International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 37 - Number 6
Year of Publication: 2012
Authors:
B.Jayanthi
Dr.K.Duraiswamy
10.5120/4614-6609

B.Jayanthi and Dr.K.Duraiswamy. Article: A Novel Algorithm for Cross Level Frequent Pattern Mining in Multidatasets. International Journal of Computer Applications 37(6):30-35, January 2012. Full text available. BibTeX

@article{key:article,
	author = {B.Jayanthi and Dr.K.Duraiswamy},
	title = {Article: A Novel Algorithm for Cross Level Frequent Pattern Mining in Multidatasets},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {37},
	number = {6},
	pages = {30-35},
	month = {January},
	note = {Full text available}
}

Abstract

Frequent pattern mining has become one of the most popular data mining approaches for the analysis of purchasing patterns. There are techniques such as Apriori and FP-Growth, which were typically restricted to a single concept level. We extend our research to discover cross - level frequent patterns in multi-level environments. Unfortunately, little research has been paid to this research area. Mining cross - level frequent pattern may lead to the discovery of mining patterns at different levels of hierarchy. In this study a transaction reduction technique with FP-tree based bottom up approach is used for mining cross-level pattern. This method is using the concept of reduced support

References

  • Agrawal R,Imienlinski T,Swami A,(1993).Mining association rules between sets of items in large databases. In Proc. Of the ACM SIGMOD Int. Conf. on Management of Data, Pages 207-216.
  • Agrawal R, and Srikant R, (1994). Fast algorithms for mining association rules. In Proc. Of the 20th Int. Conf. on very Large Databases. Pages 487-499.
  • Han .J ,Pei .J, and Yin .Y,(2000) Mining Frequent patterns without candidate generation. In Proc. Of ACM-SIGMOD Int. Conf. on Management of Data, pages 1-12.
  • Han, J., Fu, Y., Mining Multiple-Level Association Rules in Large Databases, in IEEE Transactions on Knowledge and Data Engineering, Vol. 11, No. 5, September/October 1999.
  • T.Eavis and XI Zheng, Multi-Level Frequent Pattern Mining, in Springer-Verlag Berlin Heidelberg 2009, pp. 369 – 383.
  • Dr.K.Duraiswamy and B.Jayanthi, a Novel preprocessing Algorithm for Frequent Pattern Mining in Mutidatasets, International Journal of Data Engineering,Vol. 2, No. 3, Aug 2011.
  • Han, J., Fu, Y., Discovery of Multiple-Level Association Rules from Large Databases, in Proceedings of the 21st Very Large Data Bases Conference, Morgan Kaufmann, P. 420-431, 1995.
  • Yinbo WAN, Yong LIANG, Liya DING, “Mining Multilevel Association Rules from Primitive Frequent Itemsets”, Journal of Macau University of Science and Technology, Vol.3 No.1, 2009
  • Thakur, R. S., Jain, R. C., Pardasani, K. R., Mining Level-Crossing Association Rules from Large Databases, in the Journal of Computer Science 2(1), P. 76-81, 2006.
  • R.E.Thevar, R.Krishnamoorthy, A New Approach of Modified Transaction Reduction Algorithm For mining Frequent Itemset, proceedings of IEEE Workshop on Data mining and Artificial Intelligence, 2008.
  • Rajkumar.N, Karthik.M.R, Sivanada.S.N, “Fast Algorithm for mining multilevel Association Rules,”IEEE Trans. Knowledge and Data Engg., Vol.2 pp. 688-692, 2003.
  • Pratima Gautham, Pardasani, K. R., “Algorithm for Efficient Multilevel Association Rule Mining”, International Journal of Computer Science and Engineering, Vol.2 pp. 1700-1704, 2010