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Comparative Analysis of Variations of Ant-Miner by Varying Input Parameters
| 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
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
- Han J. , Kamber M. : Data Mining – Concepts and Techniques
- Dorigo, M. & Stutzle, T. (2004). Ant Colony Optimization. Cambridge, MA: MIT Press.
- Singler J. , Atkinson B. : Data Mining using Ant Colony Optimization
- Witten, H. , Frank, E. : Data Mining: Practical Machine Learning Tools and Techniques. 2nd Edn. Morgan Kaufmann (2005)
- Freitas, A. , Parpinelli, R. , Lopes, H. : Ant colony Algorithms for data mining. To apper in Encyclopedia of Info. Sci. & Tech. 2nd Ed (2008)
- Parpinelli, R. , Lopes, H. , Freitas, A. : Data mining With an ant colony optimization algorithm. IEEE Transactions on Evolutionary Computation 6(4) (2002) 321–332
- Quinlan, J. : C4. 5: Programs for Machine Learning. Morgan Kaufmann (1993)
- Clark, P. , Niblett, T. : The CN2 rule induction Algorithm. Machine Learning 3(4) (1989) 261–283
- Swaminathan, S. : Rule induction using ant colony Optimization for mixed variable attributes. Master's thesis, Texas Tech University (2006)
- Liu, H. , Hussain, F. , Tan, C. , Dash, M. : Discretization: An enabling technique Data Mining and Knowledge Discovery 6 (2002) 393–423
- Asuncion, A. , Newman, D. : UCI machine Learning repository.
- 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
- 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
- L. Breiman, J. Friedman, C. J. Stone, and R. A. Olshen Classi?cation and Regression Trees. Chapman and Hall, 1984.
- 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.
- 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.
- 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.
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