Analysis of clustering technique for the diabetes dataset using the training set parameter

Abstract: The Clustering technique is used to place data elements into related groups without advance knowledge of the group description. The clustering technique belongs to an unsupervised learning and it is used to discover a new set of categories. The clustering technique groups the data instances in to subsets in such a manner that similar instances are grouped together while different instances belong to different groups. This paper represents the performance of three clustering algorithms such as Hierarchical clustering, Density based clustering and K Means clustering algorithm. The Diabetes dataset is used for the comparison of those clustering algorithms based on the performance of execution time and the number of clustered instances.

Keywords: Hierarchical clustering, Density based clustering, K Means clustering, Diabetes dataset, Training set, Clustering.