CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Analysis of MFGA to Extract Interesting Rules

by Mrinalini Rana, P S Mann
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 84 - Number 3
Year of Publication: 2013
Authors: Mrinalini Rana, P S Mann
10.5120/14555-2653

Mrinalini Rana, P S Mann . Analysis of MFGA to Extract Interesting Rules. International Journal of Computer Applications. 84, 3 ( December 2013), 15-21. DOI=10.5120/14555-2653

@article{ 10.5120/14555-2653,
author = { Mrinalini Rana, P S Mann },
title = { Analysis of MFGA to Extract Interesting Rules },
journal = { International Journal of Computer Applications },
issue_date = { December 2013 },
volume = { 84 },
number = { 3 },
month = { December },
year = { 2013 },
issn = { 0975-8887 },
pages = { 15-21 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume84/number3/14555-2653/ },
doi = { 10.5120/14555-2653 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:59:57.997924+05:30
%A Mrinalini Rana
%A P S Mann
%T Analysis of MFGA to Extract Interesting Rules
%J International Journal of Computer Applications
%@ 0975-8887
%V 84
%N 3
%P 15-21
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents a Genetic algorithm based association rule mining in which multi fitness functions are used. Genetic algorithm is used for performing global search. This proposed algorithm generates intersecting association rules from dataset. A fitness function with parameter support is defined for generating frequent itemsets and then other parameters like confidence, lift, leverage etc are used for defining second fitness function for generating association rules. The proposed algorithm is compared with classical Apriori algorithm and also with existing Genetic algorithm for association rule mining on the basis of metrics Support Count, Confidence count, and rule accuracy. Comparisons are also made on different generations.

References
  1. Ashish Ghosh, Bhabesh Nath," Multi-objective rule mining using genetic algorithms", Information Sciences, Elsevier, Vol. 163, pp. 123–133, 2004.
  2. Manish Saggar, Ashish Kumar Agrawal, Abhimanyu Lad, " Optimization of Association Rule Mining using Improved Genetic Algorithms", IEEE International Conference on Systems, Man and Cybernatics, pp. 3725-3729 , 2004.
  3. Bilal Alata¸ S• Erhan Akin," An efficient genetic algorithm for automated mining of both positive and negative quantitative association rules", Soft Computing, Springer-Verlag, Vol. 10, pp. 230–237, 2005.
  4. Virendra Kumar Shrivastava1 Dr. Parveen Kumar, Dr. K. R. Pardasani, "Extraction of Interesting Association Rules using GA Optimization", Global Journal of Computer Science and Technology, Vol. 10, Issue 5, Ver. 1. 0, pp. 81-84, July 2010.
  5. Amy H. L. Lim a, Chien-Sing Lee a, Murali Raman," Hybrid genetic algorithm and association rules for mining workflow best practices", Expert Systems with Applications, Elsevier , Vol. 39, pp. 10544–10551, 2012.
  6. Jesmin Nahar a, Tasadduq Imama, Kevin S. Tickle a, Yi-Ping Phoebe Chen," Association rule mining to detect factors which contribute to heart disease in males and females ",Expert Systems with Applications, Elsevier , Vol. 40,pp. 1086–1093,2013.
  7. Dong Gyu Lee, Kwang Sun Ryu, Mohamed Bashir, Jang-WhanBae, Keun Ho Ryu, " Discovering Medical Knowledge using Association Rule Mining in Young Adults with Acute Myocardial Infarction", Journal of Medical Systems, Springer, Vol. 37,Issue 2, 2013.
  8. B. Minaei-Bidgoli, R. Barmaki, M. Nasiri, " Mining numerical association rules via multi-objective genetic algorithms", Information Sciences, Elsevier, 2013.
  9. Basheer Mohamad, Al-Maqaleh, " Discovering Interesting Association Rules: A Multi-objective Genetic Algorithm Approach", International Journal of Applied Information Systems, Vol. 5, No. 3, pp. 47-52,February 2013.
  10. Bettahally, N. Keshavamurthy, Asad M. Khan ,Durga Toshniwal, "Privacy preserving association rule mining over distributed databases using genetic algorithm", Neural Computing and Applications, Springer-Verlag, 2013.
  11. J. Alcalá-Fdez1, A. Fernández1, J. Luengo1, J. Derrac1,S. García2, L. Sánchez3 And F. Herrera1," KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework",J. of Mult. -Valued Logic & Soft Computing, Vol. 17, pp. 255–287, 2011.
  12. By Jyothi Pillai, O. P. Vyas, "Encapsulation of Soft Computing Approaches within Itemset Mining – A Survey", Global Journal of Computer Science and Technology Software & Data Engineering, Vol. 12 Issue 15 Versions 1. 0, 2012.
  13. Chengfeng Jian, Fang Li, "An Improved Virus Evolutionary Genetic Algorithm For Workflow Mining ", Journal of Theoretical and Applied Information Technology, Vol. 47 No. 1, pp. 406-411, 2013.
  14. Sanjeev Sharma, Vivek Bahde, Sudhir Sharma, "Optimization of Association Rules Using Genetic Algorithm", International Journal of Soft Computing, pp. 75-79, 2007.
  15. Chad Creighton, Samir Hanash, "Mining gene expression databases for association rules", Bioinformatics, Vol. 19 no. 1, pp. 79-86, 2003.
  16. Nicolò Flugy, Jesús Alcalá-Fdez, Andrea Bonarini, Francisco Herrera, " Evolutionary Extraction of Association Rules: A Preliminary Study on their Effectiveness", Springer-Verlag, pp. 646–653, 2009.
  17. Ming-Hsiung Ying, Shao-Hsuan Huang, Luen-Ruei Wu, "An Item Selection Strategy Based on Association Rules and Genetic Algorithms", JOURNAL OF SOFTWARE, Vol. 5, No. 12, pp. 1378-1383, 2010.
  18. Rahul Malhotra, Narinder Singh, Yaduvir Singh, "Genetic Algorithms: Concepts, Design for Optimization of Process Controllers", Computer and Information Science, Vol. 4, No. 2, pp. 39-54, March 2011.
  19. Wenxiang Dou, Jinglu Hu, Kotaro Hirasawa and Gengfeng Wu, " Quick Response Data Mining Model Using Genetic Algorithm ", SICE Annual Conference, pp. 1214-1219, 2008.
  20. Rupali Haldulakar, Prof. Jitendra Agrawal, "Optimization of Association Rule Mining through Genetic Algorithm", International Journal on Computer Science and Engineering, Vol. 3 No. 3, pp. 1252-1259, March 2011.
  21. Soumadip Ghosh, Sushanta Biswas, Debasree Sarkar, Partha Pratim Sarkar, "Mining Frequent Itemsets Using Genetic Algorithm", International Journal of Artificial Intelligence & Applications, Vol. 1, No. 4, pp. 133-143 , October 2010.
  22. Anandhavalli M. , Suraj Kumar Sudhanshu, Ayush Kumar, Ghose M. K. , "Optimized association rule mining using genetic algorithm", Advances in Information Mining, Vol. 1, Issue 2 , pp. 01-04, 2009.
  23. Farah Hanna AL-Zawaidah, Yosef Hasan Jbara, Marwan AL-Abed Abu-Zanona, " An Improved Algorithm for Mining Association Rules in Large Databases", World of Computer Science and Information Technology Journal, Vol. 1, No. 7, pp. 311-316, 2011.
  24. Pradeep Singh Raghav, Nikhil Singh, Satendra Singh Thakur, Priyank Jain, "Optimization Of Multiple Relational Classification Algorithms Using Genetic Algorithm", International Journal of Computer Science and Communication, Vol. 3, No. 1, pp. 215-217, January-June 2012.
  25. D. Kerana Hanirex, Dr. A. Kumaravel, " An Efficient Partition and Two Dimensional Approach For Mining Frequent Itemsets", International Journal of Technological Synthesis and Analysis, Vol. 1 Issue 1, pp. 14-17, December 2012.
  26. Jaishree Singh, Hari Ram, Dr. J. S. Sodhi, "Improving Efficiency of Apriori Algorithm using Transaction Reduction", International Journal of Scientific and Research Publications, Vol. 3, Issue 1, pp. 1-4, January 2013.
  27. Huan Wu, Zhigang Lu, Lin Pan, Rongsheng Xu, "An Improved Apriori-based Algorithm for Association Rules Mining", IEEE Sixth International Conference on Fuzzy Systems and Knowledge Discovery, pp. 51-55 ,2009.
  28. D. Magdalene Delighta Angeline, I. Samuel Peter James, "Association Rule Generation Using Apriori Mend Algorithm for Student's Placement", International Journal of Emerging Sciences, Vol. 2(1), pp. 78-86, March 2012.
  29. R. Uday kiran, P Krishna Reddy, "An Improved Multiple Minimum Support Based Approach to Mine Rare Association Rules", IEEE Symposium on Computational Intelligence and Data Mining, 2009.
  30. Indira K, Kanumani S, "Performance Analysis of Genetic Algorithm for Mining Association Rules",IJCSI, Vol. 9, Issue2, No 1 PP. 368 – 376, 2012.
  31. Anubha Sharma, Nirupma Tivari, "A Survey of Association Rule Mining Using Genetic Algorithm, International Journal of Computer Applications and Information Technology, Vol. 1, Issue1, pp. 5-11, 2012.
  32. Soumadip Ghosh, Sustanta Biswas, Debasree Sarkar, P. P. Sarkar, "Association Rule Mining Algorithm and Genetic Algorithm: A Comparative Study", International Conference on Emerging Applications of Information technology, pp. 202 -205, 2012.
  33. Wanjun Yu, Xiaochun Wang, Fangyi Wang, Erkang Wang, Bowen Chen, "The Research Of Improved Apriori Algorithm for Mining Association Rules", IEEE International Conference on Communication Technology Proceeding, China, pp. 513- 516, 2008.
  34. Hong GUO, Ya ZHOU, "An Algorithm for Mining Association Rules Based on Improved Genetic Algorithm and its Application", Third International Conference on Genetic and Evolutionary Computing, pp. 117 – 120, 2009.
  35. K. Y. Fung, C. K Kwong, K. M. W. Siu, K. M. Yu, "A multi-objective genetic algorithm approach to rule mining for affective product design", Expert System with Application, Elsevier, Vol. 39, pp. 7411 – 7419, 2012
Index Terms

Computer Science
Information Sciences

Keywords

Multi-Fitness Function Genetic algorithm (MFGA) Apriori algorithm Genetic Algorithm Crossover Probability Fitness function Support Confidence Lift Leverage Coverage.

Powered by PhDFocusTM