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Reseach Article

Mining Frequent Itemsets from Large Data Sets using Genetic Algorithms

Published on None 2011 by R. Vijaya Prakash, Dr. Govardhan, Dr. S.S.V.N. Sarma
Artificial Intelligence Techniques - Novel Approaches & Practical Applications
Foundation of Computer Science USA
AIT - Number 4
None 2011
Authors: R. Vijaya Prakash, Dr. Govardhan, Dr. S.S.V.N. Sarma
7471d434-ceaf-4f7a-bfac-2881a9e4dd24

R. Vijaya Prakash, Dr. Govardhan, Dr. S.S.V.N. Sarma . Mining Frequent Itemsets from Large Data Sets using Genetic Algorithms. Artificial Intelligence Techniques - Novel Approaches & Practical Applications. AIT, 4 (None 2011), 1-6.

@article{
author = { R. Vijaya Prakash, Dr. Govardhan, Dr. S.S.V.N. Sarma },
title = { Mining Frequent Itemsets from Large Data Sets using Genetic Algorithms },
journal = { Artificial Intelligence Techniques - Novel Approaches & Practical Applications },
issue_date = { None 2011 },
volume = { AIT },
number = { 4 },
month = { None },
year = { 2011 },
issn = 0975-8887,
pages = { 1-6 },
numpages = 6,
url = { /specialissues/ait/number4/2842-223/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Special Issue Article
%1 Artificial Intelligence Techniques - Novel Approaches & Practical Applications
%A R. Vijaya Prakash
%A Dr. Govardhan
%A Dr. S.S.V.N. Sarma
%T Mining Frequent Itemsets from Large Data Sets using Genetic Algorithms
%J Artificial Intelligence Techniques - Novel Approaches & Practical Applications
%@ 0975-8887
%V AIT
%N 4
%P 1-6
%D 2011
%I International Journal of Computer Applications
Abstract

Association Rules are the most important tool to discover the relationships among the attributes in a database. The existing Association Rule mining algorithms are applied on binary attributes or discrete attributes, in case of discrete attributes there is a loss of information and these algorithms take too much computer time to compute all the frequent itemsets. By using Genetic Algorithm (GA) we can improve the generation of Frequent Itemset for numeric attributes. The major advantage of using GA in the discovery of frequent itemsets is that they perform global search and its time complexity is less compared to other algorithms as the genetic algorithm is based on the greedy approach. The main aim of this paper is to find all the frequent itemsets from given data sets using genetic algorithm.

References
  1. Agrawal, R., Imielinski. T., Swami, A.: Mining association rules between sets of items in large databases. Proc. ACM SIGMOD. (1993) 207–216, Washington, D.C.
  2. Chen M.S., Han J. and Yu P.S (1996) Data Mining : An Overview from a Database Prospective, IEEE Trans.Knowledge and Data Eng., 866-883
  3. Agarwal R, Imielinski. T., Swami, (1993) Database Mining: a performance prospective, IEEE Transaction on Knowledge and Data Engineering 5(6), 914-925.
  4. Agrawal, R., Srikant, R: Fast Algorithms for Mining Association Rules. Proc. Of the VLDB Conference (1994) 487–489, Santiago (Chile)
  5. Pei M., Goodman E.D.,Punch F. (2000) Feature extraction using Genetic Algorithm, Case Center for Computer Aided Engineering and Manufacturing W. Department of Computer Science
  6. Stuart J. Russel, Peter Novig (2008) Artificial Intellegence: A Modern Approach
  7. Goldberg, D.E: Genetic algorithms in search, optimization and machine learning. Addison-Wesley. (1989)
  8. Han J., Kamber M. Data Mining Concepts & Techniques, Morgan & Kaufmann, 2000.
  9. Pujari A.K., Data Mining Technique, Universities Press, 2001
  10. Anandhavalli M, Suraj Kumar Sudhanshu, Ayush Kumar and Ghose M.K. (2009) Optimized Association Rule Mining using Genetic Algorithm, Advances in Information Mining, ISSN:0975-3265, Volume 1, Issue 2, 2009, pp-01-04.
  11. Markus Hegland. The Apriori Algorithm – a Tutorial, CMA, Australian National University, WSPC/Lecture Notes Series, 22-27, March 30, 2005.
  12. Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L.: Discovering Frequent Closed Itemsets for Association Rules
  13. Park, J. S., Chen, M. S., Yu. P.S.: An Effective Hash Based Algorithm for Mining Association Rules. Proc. of the ACM SIGMOD Int’l Conf. on Management of Data (1995) San Jos´e, CA
  14. Savarese, A., Omiecinski, E., Navathe, S.: An efficient algorithm for mining association rules in large databases. Proc. of the VLDB Conference, Zurich, Switzerland (1995)
  15. Srikant, R, Agrawal, R.: Mining Quantitative Association Rules in Large Relational Tables. Proc. of the ACM SIGMOD (1996) 1–12
  16. Wang, K., Tay. S.H., Liu, B.: Interestingness-Based Interval Merger for Numeric Association Rules. Proc. 4th Int. Conf. KDD (1998) 121–128.
Index Terms

Computer Science
Information Sciences

Keywords

Genetic Algorithm (GA) Association Rule Mining(ARM) Frequent itemset Data Mining(DM) Frequent itemset Data Mining(DM)