Abstract
Rule acquisition is a technique of data mining that is used to deduce inferences from large databases. These inferences cannot be noticed easily without data mining. Genetic algorithms (GAs) are considered as a global search approach for optimization problems. Through the proper evaluation strategy, the best “chromosome” can be found from the numerous genetic combinations. In the self-adaptive genetic algorithm, its main thought is to let control parameter (crossover rate, mutation rate) adjusted adaptively within the proper range, thus achieve a more optimum solution. It is proved that the self-adaptive genetic algorithm is with excellent convergence and higher precision than the traditional genetic algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Collard, M., Francisi, D.: Evolutionary Data Mining: An Overview of Genetic-Based Algorithms. In: 8th IEEE International Conference on Emerging Technologies and Factory Automation, vol. 1, pp. 3–9 (2001)
Chiu, C., Hsu, P.-l.: A Constraint Based Genetic algorithm approach for Mining Classification Rules. IEEE Transactions on Systems, Man and Cybernetics 35, 305–320 (2005)
Saggar, M., Agarwal, A.K., Lad, A.: Optimization of Association Rule Mining using Improved Genetic Algorithms. IEEE, Transaction on System, Man and Cybernetics 4, 3725–3729 (2004)
Zhu, X., Yu, Y., Guo, X.: Genetic Algorithm Based on Evolution Strategy and the Application in Data Mining. In: First International Workshop on Education Technology and Computer Science, ETCS 2009, vol. 1, pp. 848–852 (2009)
Cattral, R., Oppacher, F., Dwego, D.: Rule Acquisition with Genetic Algorithm. In: Congress on Evolutionary Computation, CEC 1999, vol. 1 (1999)
Dai, S., Gao, L., Zhu, Q., Zhu, C.: A Novel Genetic Algorithm Based on Image Databases for Mining Association Rules. In: IEEE Conference on Computer and Information Science, pp. 977–980 (2007)
Wu, Y.-T., An, Y.J., Geller, J., Wu, Y.T.: A Data Mining Based Genetic Algorithm. In: IEEE Workshop on Software Technologies for Future Embedded and Ubiquitous Systems (2006)
Li, J., Feng, H.R.: A Self-Adaptive Genetic Algorithm Based on Real Code, pp. 1–4. Capital Normal University, CNU (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Indira, K., Kanmani, S., Gaurav Sethia, D., Kumaran, S., Prabhakar, J. (2011). Rule Acquisition in Data Mining Using a Self Adaptive Genetic Algorithm. In: Nagamalai, D., Renault, E., Dhanuskodi, M. (eds) Trends in Computer Science, Engineering and Information Technology. CCSEIT 2011. Communications in Computer and Information Science, vol 204. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24043-0_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-24043-0_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24042-3
Online ISBN: 978-3-642-24043-0
eBook Packages: Computer ScienceComputer Science (R0)