Mining Association rules and Differential Privacy Preservation using Randomization
Keywords:
Discrimination, Association, CPAR, GC, DDPD, DDPP, IDPD, IDPP, DRP, IRPAbstract
Paper herewith proposes an optimal predictive class association rule mining techniques for extracting the minimum rule having same predictive power of complete predictive class association rule by using predictive association rule set instead of complete class association rule , proposed methodologies in this paper can avoid the redundant and non-useful computation that would otherwise be required or needed for the mining of predictive class association rules and therefore improving the efficiency and effectiveness of the mining process significantly. Paper herewith presents an efficient and effective algorithm framework for mining the optimal predictive class association rule dataset by using CPAR before they are actually generated. In this paper, techniques have been implemented and obtained experimental results demonstrate that the algorithm generates the optimal class association rule set. Hence paper herewith propose a new data classification approach, Classification based on the Predictive Association Rules, which mainly combines the advantages and knowledge of both traditional rule-based and associative classification. Instead of generating the large number of candidate class association rules as in associative classification techniques, CPAR usually adopts a greedy algorithm for generating rules directly from the training dataset.
References
Jiuyong Lia, Hong Shenb, Rodney Topor , "Mining the optimal class association rule set" Received 2 April 2001 accepted 22 November 2001.
Xiaoxin Yin Jiawei Han , "CPAR: Classification based on Predictive Association Rules " University of Illinois at Urbana-Champaign {xyin1, hanj}@cs.uiuc.edu.
Asmita Kashid, Vrushali Kulkarni and Ruhi Patankar, “ Discrimination Prevention using Privacy Preserving Techniques”, International Journal of Computer Applications (0975 – 8887) Volume 120 – No.1, June 2015.
Kamal D. Kotapalle and Shyam Gupta, “Discrimination Prevention and Privacy Preservation in Data Mining”, www.ijird.com July, 2014 Vol 3 Issue 7 INTERNATIONAL JOURNAL.
Rakesh Agrawal Tomasz Imielinski Arun Swami , "Mining Association Rules between Sets of Items in Large Databases" , IBM Almaden Research Center 650 Harry Road, San Jose, CA 95120 , 1993 ACM SIGMOD Conference Washington DC, USA, May 1993.
Bing Liu Wynne Hsu Yiming Ma , "Integrating Classification and Association Rule Mining", Department of Information Systems and Computer Science National University of Singapore ,Lower Kent Ridge Road, Singapore 119260.
Sergey Brin Rajeev Motwani Craig Silverstein "Beyond Market Baskets: Generalizing Association Rules to Correlations”, Department of Computer Science Stanford University Stanford,CA 94305.
K. Ali, S. Manganaris, R. Srikant, Partial classification using association rules, in: D. Heckerman, H. Mannila, D. Pregibon, R. Uthurusamy (Eds.), Proceedings of the Third International Conference on Knowledge Discovery and Data Mining (KDD-97), AAAI Press, Menlo Park, CA, 1997, p. 115.
R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, A.I. Verkamo, Fast discovery of association rules, in: U. Fayyad (Ed.), Advances in Knowledge Discovery and Data Mining, MIT Press, Cambridge, 1996.
M. Houtsma, A. Swami, Set-oriented mining of association rules in relational databases, 11th International Conference Data engineering, 1995.
J.S. Park, M. Chen, P.S. Yu, An effective hash based algorithm for mining association rules, ACM SIGMOD International Conference Management of Data, May, 1995.
J. R. Quinlan , "Improved Use of Continuous Attributes in C4.5”, Basser Department of Computer Science, University of Sydney, Sydney Australia 2006 , Journal of Articial Intelligence Research 4 (1996) 77-90 Submitted 10/95; published 3/96
P. Clark, R. Boswell, Rule induction with CN2: Some recent improvements, in: Y. Kodratoff (Ed.), Machine Learning—EWSL- 91, 1991.
Fabrice Muhlenbachand Ricco Rakotomalala, "Discretization of Continuous Attributes " , Université Jean Monnet – Saint-Etienne, France.
Alexandre Evfimievski,"Randomization in Privacy Preserving Data Mining”, Cornell University,Ithaca, NY 14853, USA Volume 4, Issue 2.
S. Hajian and J. Domingo, "A Methodology for Direct and Indirect Discrimination prevention in data mining." IEEE transaction on knowledge and data engineering, VOL. 25, NO. 7, pp. 1445-1459, JULY 2013.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
