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.
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Karegowda, A.G., Vidya, T., Shama, Jayaram, M.A., Manjunath, A.S. (2013). Improving Performance of K-Means Clustering by Initializing Cluster Centers Using Genetic Algorithm and Entropy Based Fuzzy Clustering for Categorization of Diabetic Patients. In: Kumar M., A., R., S., Kumar, T. (eds) Proceedings of International Conference on Advances in Computing. Advances in Intelligent Systems and Computing, vol 174. Springer, New Delhi. https://doi.org/10.1007/978-81-322-0740-5_108
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DOI: https://doi.org/10.1007/978-81-322-0740-5_108
Publisher Name: Springer, New Delhi
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