Research Article | OPEN ACCESS
Developing Clustering Based on Genetic Algorithm for Global Optimization
Khaled Batiha and Zeinab M. Olimat
Prince Hussein Bin Abdullah Faculty of Information Technology, Al al-Bayt University, Jordan
Research Journal of Applied Sciences, Engineering and Technology 2015 3:336-342
Received: April 7, 2015 | Accepted: May 22, 2015 | Published: September 25, 2015
Abstract
Nowadays, databases are widely used over the world. The huge amount of data requires modern methods to make it useful meaning of information, clustering is one of the techniques that collects similar objects then put them in groups. Clustering is an approach appropriate for extracting useful meaning in large database. K-mean clustering is an algorithm characterized by simplicity and easy to implement and provides good results. However, it suffers from being trapped in local optimal solution. Some hybrid between two algorithms aims to combine the advantages of two algorithms to make optimization. In this thesis, we propose applying the same hybrid between k-mean clustering and Differential Evolution (DE) called Clustering based Differential Evolution CDE, but in the proposed method, we use Genetic Algorithm (GA) instead of Differential Evolution to find a globally optimal solution. This proposed method called Clustering based on Genetic Algorithm for Global Optimization (CGAGO), then we compare between them. In addition, we use a parameter called cluster period to improve k-mean clustering, in terms of finding the global optimum. Moreover, we test eleven Benchmark functions to validate the proposed method. Experimental results show that the proposed method CGAGO is slightly better and effective than CDE.
Keywords:
Datamining, genetic algorithm, global optimization, k-mean clustering,
References
-
Cai, Z., W. Gong, C. Ling and H. Zhang, 2011. A Clustering-based differential evolution for global optimization. Appl. Soft Comput., 11(1): 1363-1379.
CrossRef - Damavandi, N. and S. Safavi-Naeini, 2005. A hybrid evolutionary programming method for circuit optimization. IEEE Transaction on Circuits and Systems, 52(5): 902-910.
CrossRef
-
Elmasri, R and S.B. Navathe, 2010. Fundamentals of Database Systems. 6th Edn., Addison-Wesley, Boston, pp: 1035-1057.
- Goldberg, D., 1989. Genetic Algorithms in Search: Optimization and Machine Learning. 1st Edn., Addison-Wesley, New York.
- Han, J. and M. Kamber, 2011. Data Mining: Concepts and Techniques. 3rd Edn., Morgan Kaufmann, USA, pp: 1-32.
-
Hand, D., H. Mannila and P. Smyth, 2001. Principles of Data Mining. MIT Press, Cambridge, Mass, ISBN: 026208290X, pp: 546.
PMid:11382822
- Jiang, J., J. Wang, X. Chu and R. Yu, 1997. Clustering data using a modified Integer Genetic Algorithm (IGA). Anal. Chim. Acta, 354(1-3): 263-274.
CrossRef
-
KumarMeena, Y., Shashank and V.P. Singh, 2012. Text documents clustering using genetic algorithm and discrete differential evolution. Int. J. Comput. Appl., 43(1): 16-19.
- Liu, J. and J. Lampinen, 2005. A fuzzy adaptive differential evolution algorithm. Soft Computing. 2005; 9(6): 448-462.
CrossRef
-
Rana, S., S. Jasola and R. Kumar, 2010. A hybrid sequential approach for data clustering using k-mean and particle swarm optimization algorithm. Int. J. Eng. Sci. Technol., 2(6): 167-176.
- Rusell, S.I. and P. Norving, 2003. Artificial Intelligence: Modern Approach. 2nd Edn., Pearson Education, London, pp: 116-119.
- Sumathi, S. and S.N. Sivanandam, 2006. Introduction to Data Mining and its Applications. Springer, Berlin, Heidelberg, New York, ISBN: 3540343504, pp: 828.
CrossRef
- UCI, 2014. Retrieved from: http://archive.ics. uci.edu/ml/. (Accessed on: March 25, 2014).
Direct Link
-
Weise, T., 2009. Global Optimization Algorithm-theory and Application. 2nd Edn., Retrieved from: http://www.it-weise.de/. (Accessed on: February 10, 2009).
Direct Link
Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
|
|
![](http://www.maxwellsci.com/images/RJASET-maxw.jpg) |
ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
![Submit Manuscript](../images/sub.jpg) |
Information |
|
|
|
Sales & Services |
|
|
|