Inter Cluster Distance Management Model With Optimal Centroid Estimation for K-Means Clustering Algorithm
Clustering techniques are used to group up the transactions based on the relevancy. Cluster analysis is one of the primary data analysis method. The clustering process can be done in two ways such that Hierarchical clusters and partition clustering. Hierarchical clustering technique uses the structure and data values. The partition clustering technique uses the data similarity factors. Transactions are partitioned into small groups. K-means clustering algorithm is one of the widely used clustering algorithms. Local cluster accuracy is high in the K-means clustering algorithm. Inter cluster relationship is not concentrated in the K-means algorithm. K-means clustering algorithm requires the cluster count as the major input. The system chooses random transactions are initial centroid for each cluster.