Journal of Applied Information Science

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Clustering and Classifying Diabetic Data Sets Using K-means Algorithm


Affiliations
1 Department of Computer Applications, Bannari Amman Institute of Technology, Sathyamangalam, Tamil Nadu., India
2 Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathyamangalam, Tamil Nadu., India
     

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The k-means algorithm is well known for its efficiency in clustering large data sets. However, working only on numeric values prohibits it from being used to cluster real world data containing categorical values. In this paper we present the Classification of diabetic's data set and the k-means algorithm to categorical domains. Before classify the data set preprocessing of data set is done to remove the noise in the data set. We use the missing value algorithm to replace the null values in the data set. This algorithm is also used to improve the classification rate and cluster the data set using two attributes namely plasma and pregnancy attribute.

Keywords

Classification, Cluster Analysis, Clustering Algorithms, Categorical Data, Pre-processing
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  • Clustering and Classifying Diabetic Data Sets Using K-means Algorithm

Abstract Views: 871  |  PDF Views: 6

Authors

M. Kothainayaki
Department of Computer Applications, Bannari Amman Institute of Technology, Sathyamangalam, Tamil Nadu., India
P. Thangaraj
Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathyamangalam, Tamil Nadu., India

Abstract


The k-means algorithm is well known for its efficiency in clustering large data sets. However, working only on numeric values prohibits it from being used to cluster real world data containing categorical values. In this paper we present the Classification of diabetic's data set and the k-means algorithm to categorical domains. Before classify the data set preprocessing of data set is done to remove the noise in the data set. We use the missing value algorithm to replace the null values in the data set. This algorithm is also used to improve the classification rate and cluster the data set using two attributes namely plasma and pregnancy attribute.

Keywords


Classification, Cluster Analysis, Clustering Algorithms, Categorical Data, Pre-processing

References