9th International Science Congress (ISC-2019).  International E-publication: Publish Projects, Dissertation, Theses, Books, Souvenir, Conference Proceeding with ISBN.  International E-Bulletin: Information/News regarding: Academics and Research

A New Approach to Mine Frequent Itemsets

Author Affiliations

  • 1Computer Engineering Department, UVPCE, Kherva, Gujarat, INDIA
  • 2Computer Engineering Department, UVPCE, Kherva, Gujarat, INDIA

Res. J. Engineering Sci., Volume 1, Issue (1), Pages 14-18, July,26 (2012)

Abstract

Mining frequent patterns in transaction databases and many other kinds of databases has been studied popularly in data mining research. Methods for efficient mining of frequent itemsets have been studied extensively by many researchers. However, the previously proposed methods still encounter some performance bottlenecks when mining databases with different data characteristics. The time required for generating frequent itemsets plays an important role. And also the poor efficiency of counting candidate itemset’s support count. In this study, we propose a new frequent itemsets tree (FI-tree) structure, which is used for storing frequent itemsets and their Tid sets. A distinct feature of this method is that it has runs fast in different data characteristics. Our study shows that a new approach has high performance in various kinds of data, outperforms the previously developed algorithms in different settings, and is highly scalable in mining different databases.

References

  1. Agrawal R., Imielienski T. and A. Swami,, Mining Association Rules between Sets of Items in Large Databases,, Proc. Conf. on Management of Data, 207–216 (1993)
  2. Raorane A.A., Kulkarni R.V. and Jitkar B.D.,, Association Rule – Extracting Knowledge Using Market Basket Analysis,, Res. J. Recent Sci., 1(2), 19-27 (2012)
  3. Shrivastava Neeraj and Lodhi Singh Swati,, Overview of Non-redundant Association Rule Mining,, Res. J. Recent Sci., 1(2), 108-112 (2012)
  4. Pramod S. and Vyas O.P.,, Survey on Frequent Item set Mining Algorithms,, In Proc. International Journal of Computer Applications (0975 - 8887), 1(15), 86–91 (2010)
  5. Agrawal R. and Srikant R.,, Fast algorithms for mining association rules,, In Proc. Int’l Conf. Very Large Data Bases (VLDB), 487–499 (1994)
  6. Park J.S., Chen M.S. and Yu P.S.,, An effective hash-based algorithm for mining association rules,, In Proc. ACM-SIGMOD Int’l Conf. Management of Data (SIGMOD), 175–186 (1995)
  7. Brin S., Motwani R, Ullman J.D. and Tsur S.,, Dynamic itemset counting and implication rules for market basket analysis, In Proc., ACM-SIGMOD Int’l Conf. Management of Data (SIGMOD), 255–264 (1997)
  8. Savasere A., Omiecinski E. and Navathe S.,, An efficient algorithm for mining association rules in large databases,, In Proc. Int’l Conf. Very Large Data Bases (VLDB), 432–443 (1995)
  9. Toivonen C.H.,, Sampling large databases for association rules,, In Proc. Int’l Conf. Very Large Data Bases (VLDB), 134–145 (1996)
  10. Borgelt C.,, Efficient Implementations of Apriori and Eclat,, In Proc. 1st IEEE ICDM Workshop on Frequent Item Set Mining Implementations (2003)
  11. Han J., Pei H. and Yin Y.,, Mining Frequent Patterns without Candidate Generation,, In Proc. Conf. on the Management of Data (2000)
  12. Pei J., Han J., Lu H., Nishio S., Tang S. and Yang D.,, H-mine: Hyper-structure mining of frequent patterns in large databases,, In Proc. Int’l Conf. Data Mining (2001)
  13. Borgelt C.,, SaM: Simple Algorithms for Frequent Item Set Mining,, IFSA/EUSFLAT 2009 conference (2009)
  14. Blake C.L. and Merz C.J.,, UCI Repository of Machine Learning Databases,, Dept. of Information and Computer Science, University of California at Irvine, CA, USA (1998)