Clustering Algorithms on Diabetes Data: Comparative Case Study
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Abstract
Data Clustering is used to extract meaningful information and plays a vital role in data mining. Its main job is to group the similar data together based on the characteristic they possess. This paper represents the performance of three clustering algorithms such as Hierarchical clustering, EM and K Means clustering algorithm.
The Diabetes dataset is used for the comparison of those clustering algorithms based on the performance. This comparative study focuses on use of data mining tool to analyze a previously obtained data set using Weka and Tanagra. The results were compared to find algorithm yields and best result presented.
The Diabetes dataset is used for the comparison of those clustering algorithms based on the performance. This comparative study focuses on use of data mining tool to analyze a previously obtained data set using Weka and Tanagra. The results were compared to find algorithm yields and best result presented.
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