Chapter

Proceedings of International Conference on Advances in Computing

Volume 174 of the series Advances in Intelligent Systems and Computing pp 899-904

Improving Performance of K-Means Clustering by Initializing Cluster Centers Using Genetic Algorithm and Entropy Based Fuzzy Clustering for Categorization of Diabetic Patients

  • Asha Gowda KaregowdaAffiliated withDepartment of Master of Computer Applications, Siddaganga Institute of Technology Email author 
  • , T. VidyaAffiliated withDepartment of Master of Computer Applications, Siddaganga Institute of Technology
  • , ShamaAffiliated withDepartment of Master of Computer Applications, Siddaganga Institute of Technology
  • , M. A. JayaramAffiliated withDepartment of Master of Computer Applications, Siddaganga Institute of Technology
  • , A. S. ManjunathAffiliated withDepartment of Master of Computer Applications, Siddaganga Institute of Technology

* Final gross prices may vary according to local VAT.

Get Access

Abstract

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.

Keywords

k-means clustering cluster center initialization Genetic algorithm Entropy based fuzzy clustering Pima Indian Diabetics