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Analysis of Various Multiobjective Genetic Approaches in Association Rule Mining

by Sonia Sharma, Vinay Chopra
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 86 - Number 16
Year of Publication: 2014
Authors: Sonia Sharma, Vinay Chopra
10.5120/15067-3430

Sonia Sharma, Vinay Chopra . Analysis of Various Multiobjective Genetic Approaches in Association Rule Mining. International Journal of Computer Applications. 86, 16 ( January 2014), 6-9. DOI=10.5120/15067-3430

@article{ 10.5120/15067-3430,
author = { Sonia Sharma, Vinay Chopra },
title = { Analysis of Various Multiobjective Genetic Approaches in Association Rule Mining },
journal = { International Journal of Computer Applications },
issue_date = { January 2014 },
volume = { 86 },
number = { 16 },
month = { January },
year = { 2014 },
issn = { 0975-8887 },
pages = { 6-9 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume86/number16/15067-3430/ },
doi = { 10.5120/15067-3430 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:04:21.546806+05:30
%A Sonia Sharma
%A Vinay Chopra
%T Analysis of Various Multiobjective Genetic Approaches in Association Rule Mining
%J International Journal of Computer Applications
%@ 0975-8887
%V 86
%N 16
%P 6-9
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Data mining is used now days by companies with a strong consumer focus. It enables these companies to know the relationships among "internal" factors such as, product positioning, price or staff skills, and "external" factors such as indicators, economic, competition, and customer demographics. The overall aim of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. In this paper, the multi-objective genetic approach for the result Comparison of Pittsburgh and Michigan approach using multi-objective genetic algorithm has been proposed, and it is shown that using Pittsburgh approach is much better than the Michigan approach.

References
  1. Indra k and kanmanis, March 2012 " Performance analysis of genetic algorithm for mining association rules" International Journal Of Computer Science, issues Vol. 9, issue 2, pp: 318-376.
  2. Rajul Anand, Abhishek Vaid, Pramod Kumar Singh 2009 "Association Rule Mining Using Multi-Objective evolutionary algorithm: Strengths and challenges" IEEE Conference, , pp:385-389.
  3. Rupali Haldulakar and Prof. Jitender Aggarwal March 2011 "optimization and association rule mining through genetic algorithm" International Journal Of Computer Sciences And Engineering Vol. 3, No. 3, , pp: 1252-1259.
  4. Jian Hu and Xing Yang Li2007 "Association rule mining using multi-objective co evolutionary algorithm" Ieee International Conference On Computational Intelligence And Security Workshop, , pp: 405-408.
  5. Basheer Mohamad, February 2013 "Discovering interesting association rules a multiobjective genetic algorithm" International Journal Of Applied Information System, Vol. 5 No. 3, pp: 47-52.
  6. Sanat Jain, Swati Ka April 2012bra "Mining and optimization of association rules using effective algorithm" International Journal Of Emerging Technology And Advanced Engineering, Vol. 2 issue 4.
  7. J. Malar Vizhi and Dr. T. Bhuvaneswari Jan 2012 " Data quality measurement on categorical data using genetic algorithm" International Journal And Determining And Knowledge Management Process, Vol. 2, 01, pp: 33-42,
  8. Sonia Sharma, vinay chopra September 2013 " Association Rule Mining: A Multi-objective Genetic Algorithm Approach Using Pittsburgh Technique" International Journal of Recent Technology and Engineering ,Vol 2 Issue 4.
Index Terms

Computer Science
Information Sciences

Keywords

Michigan Pittsburgh Multi-objective Genetic Algorithm.

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