Manuscript Number : IJSRSET16257

An Improved Association Rule Mining with Frquent Itemset Relationship Technique

Authors(2) :-Prof.Neeraj Shukla, Arpita Sen

Construction also, improvement of classifier that work with more precision and perform productively for vast database is one of the key errand of information mining methods [l7] [18]. Besides preparing dataset over and over produces huge measure of principles. It's exceptionally difficult to store, recover, prune, and sort an enormous number of standards capably before applying to a classifier [1]. In such circumstance FP is the best decision yet issue with this methodology is that it produces repetitive FP Tree. A Frequent example tree (FP-tree) is a sort of prefix tree [3] that permits the identification of repetitive (continuous) thing set restrictive of the competitor thing set era [14]. It is expected to recover the blemish of existing mining strategies. FP-Trees seeks after the gap and overcomes strategy. In this paper we have embrace the same thought of creator [17] to manage vast database. For this we have incorporated a positive and negative tenet mining idea with regular example (FP) of characterization. Our technique performs well and creates special tenets without uncertainty.

Authors and Affiliations

Prof.Neeraj Shukla
Department of Computer Science & Engineering, Gyan Ganga College of Technology Jabalpur, Madhya Pradesh, India
Arpita Sen
Department of Computer Science & Engineering, Gyan Ganga College of Technology Jabalpur, Madhya Pradesh, India

Association, FP, FP-Tree, Nagtive, Positive

  1. Agrawal R,Imielinski T,Swami A, “Mining Association Rules between Sets of Items in Large Databases, ”In:Proc of the ACM SIGMOD International conference on Management of Data,Washington DC,1993,pp,207-216.
  2. QI Zhenyu, XU Jing, GONG Dawei and TIAN He “Traversing Model Design Based on Strong-association Rule for Web Application Vulnerability Detection”, IEEE, International Conference on Computer Engineering and Technology, 2009.
  3. B.M.Rezbaul Islam and Tae-Sun Chung “An Improved Frequent Pattern Tree Based Association Rule Mining Technique”, IEEE, International Conference on Information Science and Applications (ICISA), 2011
  4. LUO XianWen and WANG WeiQing “Improved Algorithms Research for Association Rule Based on Matrix”, IEEE, International Conference on Intelligent Computing and Cognitive Informatics, 2010.
  5. R Srikant, Qouc Vu and R Agrawal “Mining Association Rules with Item Constrains”. IBM Research Centre, San Jose, CA 95120, USA.
  6. R Agrawal and R Srikant “Fast Algorithm for Mining Association Rules”. Proceedings of VLDB conference pp 487 – 449, Santigo, Chile, 1994.
  7. Han and M. Kamber.Data Mining: Concepts and Techniques. Morgan Kaufman, San Francisco, CA, 2001.
  8. Ashok Savasere, E. Omiecinski and ShamkantNavathe“An Efficient Algorithm for Mining Association Rules in Large Databases”, Proceedings of the 21st VLDB conference Zurich, Swizerland, 1995.
  9. Arun K Pujai “Data Mining techniques”, UniversityPress (India) Pvt. Ltd., 2001.
  10. S. Park, M.-S. Chen and P. S. Yu, “An effective Hash-Based Algorithm for Mining Association Rules”, Proceedings of the ACM SIGMOD, San Jose, CA, May1995, pp. 175-186.
  11. Brin, R. Motwani, J. D. Ullman and S. Tsur, “Dynamic Item set Counting and Implication Rules for Market Basket Data”, Proceedings of the ACM SIGMOD, Tucson, AZ, May 1997, pp. 255-264.
  12. Han, J. Pei, and Y. Yin, “Mining Frequent Patterns without Candidate Generation”, Proceedings of the ACM SIGMOD, Dallas, TX, May 2000, pp. 1-12.
  13. Qin Ding and gnanasekaran Sundaraj “Association rule mining from XML data”, Proceedings of the conference on data mining.DMIN’06.
  14. Silverstein, S. Brin, and R. Motwani, “Beyond Market Baskets: Generalizing Association Rules to Dependence Rules,” Data Mining and Knowledge Discovery, 2(1), 1998, pp 39–68.
  15. Yanguang Shen, Jie Liu and Jing Shen “The Further Development of Weka Base on Positive and Negative Association Rules”, IEEE, International Conference on Intelligent Computation Technology and Automation (ICICTA), 2010.
  16. Yanguang Shen,Jie Liu,Fangping Li.Application Research on Positive and Negative Association Rules Oriented Software Defects,2009 International Conference on Computational Intelligence and Software Engineering(CISE 2009)[C].Wuhan,China,December 11-13,2009.
  17. LuoJunwei and Luo Huimin “Algorithm for Classification Based  on Positive and  Negative  Class Association  Rules”, 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT), 2010.
  18. Wenmin Li, Jiawei Han and Jian Pei, "CMAR: Accurate and Efficient Classification  Based  on  Multiple  Class- Association  Rules," Proceedings  of  the  2001  IEEE  International  Conference  on  Data Mining, IEEE Press, Dec. 2001,  123-131.

Publication Details

Published in : Volume 2 | Issue 5 | September-October 2016
Date of Publication : 2016-10-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 46-52
Manuscript Number : IJSRSET16257
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

Prof.Neeraj Shukla, Arpita Sen, " An Improved Association Rule Mining with Frquent Itemset Relationship Technique, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 5, pp.46-52, September-October.2016
URL : http://ijsrset.com/IJSRSET16257
Get in Touch

Follow Us

Contact Us

IJSRSET @ Copyright 2018. All Rights Reserved.