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Efficient K-Means Algorithm for Data Clustering Using Calinski Indexing

K. Sheela, N. Kamalraj


In Data mining clustering is one of the important tools. Several research areas clustering is to be used and it describe the method for grouping the data. Describes the K-Means clustering algorithm and it has used the best validity index (Calinski index) for the attribute selection, having the value of the validity index as fitness function. Calinski index is to find the best number of clusters for the whole data set. The method is to study the maximum value of (where k is the number of clusters and is the Calinski index value for k clusters). Number of cluster can be calculated by using Calinski index values along with NMF. The rand index value and the accuracy for the Calinski value is obtained, which proves that rand index value and accuracy is better than the existing clusters.


K-Means Clustering, Calinski Index Value, Rand Index Value, WBC Dataset.

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