CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Comparative Analysis of Variations of Ant-Miner by Varying Input Parameters

by Sonal P. Rami, Mahesh H. Panchal
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 60 - Number 3
Year of Publication: 2012
Authors: Sonal P. Rami, Mahesh H. Panchal
10.5120/9673-4097

Sonal P. Rami, Mahesh H. Panchal . Comparative Analysis of Variations of Ant-Miner by Varying Input Parameters. International Journal of Computer Applications. 60, 3 ( December 2012), 25-29. DOI=10.5120/9673-4097

@article{ 10.5120/9673-4097,
author = { Sonal P. Rami, Mahesh H. Panchal },
title = { Comparative Analysis of Variations of Ant-Miner by Varying Input Parameters },
journal = { International Journal of Computer Applications },
issue_date = { December 2012 },
volume = { 60 },
number = { 3 },
month = { December },
year = { 2012 },
issn = { 0975-8887 },
pages = { 25-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume60/number3/9673-4097/ },
doi = { 10.5120/9673-4097 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:05:39.681478+05:30
%A Sonal P. Rami
%A Mahesh H. Panchal
%T Comparative Analysis of Variations of Ant-Miner by Varying Input Parameters
%J International Journal of Computer Applications
%@ 0975-8887
%V 60
%N 3
%P 25-29
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Ant Colony Optimization (ACO) studies artificial systems that take inspiration from the behavior of real ant colonies and which are used to solve discrete optimization problems. ACO can be applied to the data mining field to extract rule-based classifiers. This paper presents variations of Ant-Miner named cAnt-Miner (Ant-Miner coping with continuous attributes), which incorporates an entropy-based discretization method in order to cope with continuous attributes during the rule construction process and Ant-Tree-Miner (constructing decision trees based on ACO) which generates classifications rules always in graphical form (Decision Tree). Three algorithms (Ant-Miner, Ant-Tree-Miner and cAnt-Miner) are compared against input parameters with respect to predictive accuracy and simplicity of the discovered rules.

References
  1. Han J. , Kamber M. : Data Mining – Concepts and Techniques
  2. Dorigo, M. & Stutzle, T. (2004). Ant Colony Optimization. Cambridge, MA: MIT Press.
  3. Singler J. , Atkinson B. : Data Mining using Ant Colony Optimization
  4. Witten, H. , Frank, E. : Data Mining: Practical Machine Learning Tools and Techniques. 2nd Edn. Morgan Kaufmann (2005)
  5. Freitas, A. , Parpinelli, R. , Lopes, H. : Ant colony Algorithms for data mining. To apper in Encyclopedia of Info. Sci. & Tech. 2nd Ed (2008)
  6. Parpinelli, R. , Lopes, H. , Freitas, A. : Data mining With an ant colony optimization algorithm. IEEE Transactions on Evolutionary Computation 6(4) (2002) 321–332
  7. Quinlan, J. : C4. 5: Programs for Machine Learning. Morgan Kaufmann (1993)
  8. Clark, P. , Niblett, T. : The CN2 rule induction Algorithm. Machine Learning 3(4) (1989) 261–283
  9. Swaminathan, S. : Rule induction using ant colony Optimization for mixed variable attributes. Master's thesis, Texas Tech University (2006)
  10. Liu, H. , Hussain, F. , Tan, C. , Dash, M. : Discretization: An enabling technique Data Mining and Knowledge Discovery 6 (2002) 393–423
  11. Asuncion, A. , Newman, D. : UCI machine Learning repository.
  12. A. Abdelhalim, I. Traore, and B. Sayed. Rbdt-1:A New Rule-Based Decision Tree Generation Technique. In Proceedings of the 2009 International Symposium on Rule Interchange and Applications, pages 108–121, 2009. R. C. Barros, M. P. Basgalupp, A. C. P. L. F. de
  13. Carvalho and A. A. Freitas. A Survey of Evolutionary Algorithms for Decision-Tree Induction. To appear in IEEE Transactions Systems, Man, and Cybernetics, Part C: Applications and Reviews,2011
  14. L. Breiman, J. Friedman, C. J. Stone, and R. A. Olshen Classi?cation and Regression Trees. Chapman and Hall, 1984.
  15. Martens, D. ; De Backer, M. ; Haesen, R. , Baesens, B. & Holvoet, T. (2006). Ants constructing rule-based classifiers. In: Agraham, A. ; Grosan, C. and Ramos, V. (eds. ) Swarm Intelligence in Data Mining, 21-43. Berlin: Springer.
  16. Wang, Z. & Feng, B. (2004). Classification rule mining with an improved ant colony algorithm. AI 2004: Advances in Artificial Intelligence,LNAI 3339, 357-367. Berlin, Springer.
  17. Parpinelli, R. S. , Lopes, H. S. & Freitas, A. A. (2002b). An ant colony algorithm for classification rule discovery. In: Abbass, H. , Sarker, R. , & Newton, C. (eds. ). Data Mining: a Heuristic Approach, London: Idea Group Publishing, 191-208.
Index Terms

Computer Science
Information Sciences

Keywords

Ant colony optimization cAnt-Miner Ant-Tree-Miner decision tree

Powered by PhDFocusTM