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Positive and Negative Rule Detection In Association Rule Using Correlation Approach Technique for Refinement of Association Rule Mining

Authors(2):

Prof. Vivek Badhe, Parul Richharia
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Development and advancement of classifier that works with more exactness and perform productively for expansive database is one of the key assignment of information mining methods. Also preparing dataset over and over produces enormous measure of guidelines. It's exceptionally hard to store, recover, prune, and sort an immense number of tenets capably before applying to a classifier. In such circumstance FP is the best decision however issue with this methodology is that it creates excess FP Tree. A Frequent example tree (FP-tree) is kind of prefix tree that permits the discovery of intermittent (continuous) thing set select of the hopeful thing set era. It is foreseen to recover the blemish of existing mining strategies. FP Trees seeks after the partition and overcomes strategy. In this theory we have adjust the same thought for distinguishing visit thing set with extensive database. For this we have coordinated a positive and negative standard mining idea with continuous example calculation and connection methodology is utilized to refine the affiliation administer and give an important affiliation rules for our objective. Our strategy performs well and creates remarkable guidelines without vagueness.

Prof. Vivek Badhe, Parul Richharia

FP, Frequent Itemset, Positive Negative Rules.

  1. Nizar R.Mabrouken, C.I.Ezeife Taxonomy of Sequential Patter Mining Algorithm". In Proc. in ACM Computing Surveys, Vol 43, No 1, Article 3, November 2010.
  2. 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.
  3. 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.
  4. 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.
  5. "Data mining Concepts and Techniques" by By Jiawei Han, Micheline Kamber, Morgan Kaufmann Publishers, 2006.
  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. Borgelt. "An Implementation of the FP- growth Algorithm". Proc. Workshop Open Software for Data Mining, 1–5.ACMPress, New York, NY, USA 2005.
  8. 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.
  9. 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.
  10. 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.
  11. H. "Sampling large databases for association rules". In Proc. Int?l Conf. Very Large Data Bases (VLDB), Sept. 1996, Bombay, India, pages 134–145.
  12. 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 (VLDB), Sept. 1995, pages 432–443.
  13. 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.
  14. 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.

Publication Details

Published in : Volume 2 | Issue 5 | September-October - 2016
Date of Publication Print ISSN Online ISSN
2016-10-31 2395-1990 2394-4099
Page(s) Manuscript Number   Publisher
499-503 IJSRSET16259   Technoscience Academy

Cite This Article

Prof. Vivek Badhe, Parul Richharia, "Positive and Negative Rule Detection In Association Rule Using Correlation Approach Technique for Refinement of Association Rule Mining", International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 5, pp.499-503, September-October-2016.
URL : http://ijsrset.com/IJSRSET16259.php

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