Improving Performance of K-Means Clustering by Initializing Cluster Centers Using Genetic Algorithm and Entropy Based Fuzzy Clustering for Categorization of Diabetic Patients
Medical Data mining is the process of extracting hidden patterns from medical data. Among the various clustering algorithms, k-means is the one of most widely used clustering technique. The performance of k-means clustering depends on the initial cluster centers and might converge to local optimum. K-Means does not guarantee unique clustering because it generates different results with randomly chosen initial clusters for different runs of k-means. This paper investigates the use of two methods namely Genetic Algorithm (GA) and Entropy based fuzzy clustering (EFC) to assign k-means initial cluster centers for clustering PIMA Indian diabetic dataset. Experimental results show markable improvement of 3.06% reduction in the classification error and execution time of k-means clustering initialized by GA and EFC when compared to k-means clustering with random cluster centers.
Keywordsk-means clustering cluster center initialization Genetic algorithm Entropy based fuzzy clustering Pima Indian Diabetics
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- 1.Han, Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kauffmann Publishers, San Francisco (2001)Google Scholar
- 2.Bradley, P.S., Fayyad, U.M.: Refining initial points for k-means algorithm. In: Proceedings of the 15th International Conference on Machine Learning (1998)Google Scholar
- 3.Al-Shbour, B., Myaeng, S.-H.: Initializing K-means using Genetic Algorithm. World Academy of Science, Engineering and Technlogy 54, 114–118 (2009)Google Scholar
- 4.Jimenez, J.F., Cuevas, F.J., Carpio, J.M.: Genetic Algorithms applied to Clustering Problem and Data Mining. In: Proceedings of the 7th WSEAS International Conference on Simulation, Modeling and Optimization, pp. 219–224 (2007)Google Scholar
- 5.Hao-Jun, Lang-Haun: Genetic Algorithm-based High-dimensional Data Clustering Technique. In: Sixth International Conference on Fuzzy Systems and Knowledge Discovery, pp. 485–489 (2009)Google Scholar
- 9.Editorial, Diagnosis and Classification of Diabetes Mellitus, American Diabetes Association, Diabetes Care 27(suppl.1) (January 2004)Google Scholar
- 10.The Expert Committee on the Diagnosis and Classification of Diabetes Mellitus: Follow up report on the Diagnosis of Diabetes Mellitus. Diabetic Care 26, 3160–3167 (2003)Google Scholar
- 11.Breault, J.L.: Data Mining Diabetic Databases: Are rough Sets a Useful Addition? (2001), http://www.galaxy.gmu.edu/interface/I01/I2001Proceedings/Jbreault
- 12.Mac Queen, J.: Some methods for the classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probility, pp. 281–297. University of California Press, Berkeley (1967)Google Scholar
- 13.Goldberg, D.: Genetic Algorithms in Search, Optimization, and Machine learning. Addison Wesley (1989)Google Scholar
- 14.Rajasekaran, S., Vijayalakshmi Pai, G.A.: Genetic Algorithm based Weight Determination for Backpropogation Networks. In: Proc. of the Fourth Int. Conf. on Advanced Computing, pp. 73–79 (1996)Google Scholar