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Reseach Article

Analysis of Optimized Association Rule Mining Algorithm using Genetic Algorithm

Published on October 2014 by Shanta Rangaswami, Shobha G., Pallavi Gupta, Anusha R., Meghana H
International Conference on Information and Communication Technologies
Foundation of Computer Science USA
ICICT - Number 2
October 2014
Authors: Shanta Rangaswami, Shobha G., Pallavi Gupta, Anusha R., Meghana H
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Shanta Rangaswami, Shobha G., Pallavi Gupta, Anusha R., Meghana H . Analysis of Optimized Association Rule Mining Algorithm using Genetic Algorithm. International Conference on Information and Communication Technologies. ICICT, 2 (October 2014), 12-15.

@article{
author = { Shanta Rangaswami, Shobha G., Pallavi Gupta, Anusha R., Meghana H },
title = { Analysis of Optimized Association Rule Mining Algorithm using Genetic Algorithm },
journal = { International Conference on Information and Communication Technologies },
issue_date = { October 2014 },
volume = { ICICT },
number = { 2 },
month = { October },
year = { 2014 },
issn = 0975-8887,
pages = { 12-15 },
numpages = 4,
url = { /proceedings/icict/number2/17968-1412/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Information and Communication Technologies
%A Shanta Rangaswami
%A Shobha G.
%A Pallavi Gupta
%A Anusha R.
%A Meghana H
%T Analysis of Optimized Association Rule Mining Algorithm using Genetic Algorithm
%J International Conference on Information and Communication Technologies
%@ 0975-8887
%V ICICT
%N 2
%P 12-15
%D 2014
%I International Journal of Computer Applications
Abstract

Apriori algorithm is a classic algorithm for frequent item set mining and association rule learning over transactional databases. The algorithm determines frequent item sets, which in turn can be used to determine association rules. These rules indicate the general trends in the database. Genetic algorithm is a search heuristic that mimics the process of natural selection using a greedy approach. This heuristic is routinely used to generate useful solutions for optimization and search problems. In this paper, we apply genetic algorithm to optimize the frequent item sets generated by Apriori algorithm and identify all possible significant association rules by analyzing the working of the algorithm on real data sets.

References
  1. Agrawal, R. , Imielinski, T. , and Swami, A. Mining association rules between sets of items in large databases. In Buneman, P. , and Jajodia, S. , (eds. ). Proceedings of ACM SIGMOD Conference on Management of Data, 1993 (SIGMOD'93), 207- 216
  2. Article titled "How Amazon is leveraging Big Data", http://www. bigdata-startups. com/BigData-startup/amazon-leveraging-big-data/
  3. Anandhavalli M. , Suraj Kumar Sudhanshu, Ayush Kumar and Ghose M. K. , Optimized association rule mining using genetic algorithm, Advances in Information Mining, ISSN: 0975-3265, Volume 1, Issue 19 September 2011
  4. Mitchell, Melanie (1996). An Introduction to Genetic Algorithms. Cambridge, MA: MIT Press. ISBN 9780585030944
  5. David Beasley et al. , "An Overview of Genetic Algorithms: Part 1, Fundamentals", University Computing, 1993
  6. Dataset from https://wiki. csc. calpoly. edu/datasets/attachment/wiki/apriori/apriori. zip
  7. Pang-Ning-Tan, Vipin Kumar, Michael Steinbach, (2007) Introduction to Data Mining
Index Terms

Computer Science
Information Sciences

Keywords

Apriori Genetic Optimization Transaction Association Rule Mining