Apriori and Ant Colony Optimization of Association Rules

Association Rule mining is one of the important and most popular data mining technique. Association rule mining can be efficiently used in any decision making processor decision based rule generation. In this paper we present an efficient mining based optimization techniques for rule generation. By using apriori algorithm we find the positive and negative association rules. Then we apply ant colony optimization algorithm (ACO) for optimizing the association rules. Our results show the effectiveness of our approach.

[1]  Hitesh Gupta,et al.  Finding Frequent Pattern with Transaction and Occurrences based on Density Minimum Support Distribution , 2012 .

[2]  S. Jain,et al.  Mining & Optimization of Association Rules Using Effective Algorithm , 2012 .

[3]  Shishir K. Shandilya,et al.  A Novel J2ME Service for Mining Incremental Patterns in Mobile Computing , 2010, ICT.

[4]  Latanya Sweeney,et al.  k-Anonymity: A Model for Protecting Privacy , 2002, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[5]  Marco Dorigo,et al.  New Ideas in Optimisation , 1999 .

[6]  Luca Maria Gambardella,et al.  Ant Algorithms for Discrete Optimization , 1999, Artificial Life.

[7]  Ling Liu,et al.  A Random Rotation Perturbation Approach to Privacy Preserving Data Classification , 2005 .

[8]  Mohammed J. Zaki,et al.  Large-Scale Parallel Data Mining , 2002, Lecture Notes in Computer Science.

[9]  Anshuman Singh Sadh,et al.  Association Rules Optimization: A Survey , 2013 .

[10]  Qiu-yong Huang,et al.  Optimization Algorithm of Association Rule Mining Based on Reducing the Time of Generating Candidate Itemset , 2011, 2011 International Conference on Control, Automation and Systems Engineering (CASE).

[11]  Chris Clifton,et al.  Privacy-preserving k-means clustering over vertically partitioned data , 2003, KDD '03.

[12]  D. Shrivastava,et al.  Web Log Mining using Improved Version of Proposed Algorithm , 2011 .

[13]  L. Deshpande,et al.  Efficient Frequent Pattern Mining Techniques of Semi Structured data: a Survey , 2013 .

[15]  A. K. Dubey,et al.  Knowledge discovery with a subset-superset approach for Mining Heterogeneous Data with dynamic support , 2012, 2012 CSI Sixth International Conference on Software Engineering (CONSEG).

[16]  Mahesh Malviya,et al.  Reduction of Negative and Positive Association Rule Mining and Maintain Superiority of Rule Using Modified Genetic Algorithm , 2012 .

[17]  Debahuti Mishra,et al.  Improving RBF Kernel Function of Support Vector Machine using Particle Swarm Optimization , 2013 .

[18]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[19]  Hitesh Gupta,et al.  A Review of Density-Based clustering in Spatial Data , 2012 .

[20]  Mohammed J. Zaki.  Parallel and Distributed Data Mining: An Introduction , 1999, Large-Scale Parallel Data Mining.

[21]  Jaideep Vaidya,et al.  Knowledge and Information Systems , 2007 .

[22]  Yehuda Lindell,et al.  Privacy Preserving Data Mining , 2002, Journal of Cryptology.

[23]  Aryya Gangopadhyay,et al.  A privacy-preserving technique for Euclidean distance-based mining algorithms using Fourier-related transforms , 2006, The VLDB Journal.