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

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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.