Diabetes is one of the disease increases in humanity across the world-wide. Data mining is a process of extracting the information from a large dataset and transforms it into understandable structure. Medical data mining has been a great capability for finding hidden patterns from the large data sets of the medical dataset. The Data mining techniques used for the prediction of diseases like heart diseases, cancer, kidney stones, EEG etc. Prediction of Diabetes is an emerging and fastest growing technology in the medical analysis data. This research paper concentrates on the clustering method for grouping diabetic data based on cluster head attributes. In this paper the popular clustering algorithms K-Means, Fuzzy C-Means (FCM) and Gaussian Kernel based Fuzzy C-Means (GKFCM) clustering algorithms are selected and analyzed based on their fundamentals by using diabetes dataset. The algorithms performance is tested based on its various analyses. The results are compared with three algorithm algorithms. Finally we obtained that the GKFCM has best than K-means and FCM algorithm.

K-means, Fuzzy C-Means (FCM), Gaussian Kernel based Fuzzy C-Means (GKFCM), Clustering, and Diabetes dataset.

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