IJSRSET calls volunteers interested to contribute towards the scientific development in the field of Science, Engineering and Technology

Home > IJSRSET17311                                                     


An Efficient Approach to Mine Frequent Itemsets Using the Variant of Classic Apriori and FP-Tree

Authors(3):

Md. Towhidul Islam Robin, Ahmed Abdal Shafi Rasel, Aiasha Siddika
  • Abstract
  • Authors
  • Keywords
  • References
  • Details
As with the advancement of the information technologies, the amount of accumulated data is also increasing. It has resulted in large amount of data stored in databases, warehouses and other repositories. Thus the Data mining comes into picture to explore and analyse the databases to extract the interesting and previously unknown patterns and rules known as association rule mining. In data mining, association rule mining becomes one of the important tasks of descriptive technique which can be defined as discovering meaningful patterns from large collection of data. Mining frequent itemset is very fundamental part of association rule mining. Many algorithms have been proposed from last many decades including horizontal layout based techniques, vertical layout based techniques, and projected layout based techniques. But most of the techniques suffer from repeated database scan, Candidate generation (Apriori Algorithms), memory consumption problem (FP-tree Algorithms) and many more for mining frequent patterns. As in retailer industry many transactional databases contain same set of transactions many times, to apply this thought, in this paper we present a new technique which is combination of present Apriori (improved Apriori) and FP-tree techniques that guarantee the better performance in terms of time and memory than classical aprioi algorithm.

Md. Towhidul Islam Robin, Ahmed Abdal Shafi Rasel, Aiasha Siddika

Frequent itemset, Association rule mining, FP-tree, Apriori, Close pattern, Cluster based mining.

  1. Savasere, E. Omiecinski, and S. Navathe. "An efficient algorithm for mining association rules in large databases". In Proc. Int’l Conf. Very Large Data Bases.
  2. R, Imielinski.t, Swami.A. "Mining Association Rules between Sets of Items in Large Databases". In Proc. Int’l Conf. of the 1993 ACM SIGMOD Conference Washington DC, USA.
  3. R and Srikant.R. "Fast algorithms for mining association rules". In Proc. Int’l Conf. Very Large Data Bases (VLDB), Sept. 1994, pages 487–499.
  4. S, Motwani. R, Ullman. J.D, and S. Tsur. "Dynamic itemset counting and implication rules for market basket analysis". In Proc. ACM-SIGMOD Int’l Conf. Management of Data (SIGMOD), May 1997, pages 255–264.
  5. Borgelt. "An Implementation of the FP- growth Algorithm". Proc. Workshop Open Software for Data Mining, 1–5.ACMPress, New York, NY, USA 2005.
  6. J, Pei.J, and Yin. Y. "Mining frequent patterns without candidate generation". In Proc. ACM-SIGMOD Int’l Conf. Management of Data (SIGMOD),2000.
  7. J. S, M.S. Chen, P.S. Yu. "An effective hash-based algorithm for mining. association rules". In Proc. ACM-SIGMOD Int’l Conf. Management of Data (SIGMOD), San Jose, CA, May 1995, pages 175–186.
  8. 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 (ICDM),November 2001.
  9. Borgelt. "Efficient Implementations of Apriori and Eclat". In Proc. 1st IEEE ICDM Workshop on Frequent Item Set Mining Implementations, CEUR Workshop Proceedings 90, Aachen, Germany 2003.
  10. H. "Sampling large databases for association rules". In Proc. Int’l Conf. Very Large Data Bases (VLDB), Sept. 1996, Bombay, India, pages 134–145.
  11. Nizar R.Mabrouken, C.I.Ezeife. Taxonomy of Sequential Pattern Mining Algorithm". In Proc. in ACM Computing Surveys, Vol 43, No 1, Article 3, November,2010.
  12. Yiwu Xie, Yutong Li, Chunli Wang, Mingyu Lu. "The Optimization and Improvement of the Apriori Algorithm". In Proc. Int’l Workshop on Education Technology and Training & International Workshop on Geoscience and Remote Sensing 2008.
  13. "Data mining Concepts and Techniques" by By Jiawei Han, Micheline Kamber, Morgan Kaufmann Publishers, 2006.
  14. P Latha, DR. N.Ramaraj. "Algorithm for Efficient Data Mining". In Proc. Int’l Conf. on IEEE International Computational Intelligence and Multimedia Applications, 2007, pp. 66-70.
  15. Dongme Sun, Shaohua Teng, Wei Zhang, Haibin Zhu. "An Algorithm to Improve the Effectiveness of Apriori". In Proc. Int’l Conf. on 6th IEEE Int. Conf. on Cognitive Informatics (ICCI'07), 2007.

Publication Details

Published in : Volume 3 | Issue 1 | January-February - 2017
Date of Publication Print ISSN Online ISSN
2017-02-28 2395-1990 2394-4099
Page(s) Manuscript Number   Publisher
47-53 IJSRSET17311   Technoscience Academy

Cite This Article

Md. Towhidul Islam Robin, Ahmed Abdal Shafi Rasel, Aiasha Siddika, "An Efficient Approach to Mine Frequent Itemsets Using the Variant of Classic Apriori and FP-Tree", International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 3, Issue 1, pp.47-53 , January-February-2017.
URL : http://ijsrset.com/IJSRSET17311.php