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Analysis of Various Clustering Techniques with Centroid Initialized K-Means Clustering

S. Nisha


K-Means is one of the algorithms that solve the well known clustering problem. The algorithm classifies objects to a pre-defined number of clusters, which is given by the user (assume k clusters). The idea is to choose random cluster centers (centroid), one for each cluster. These centers are preferred to be as far as possible from each other. Starting points affect the clustering process and results. Centroid initialization plays an important role in determining the cluster assignment in effective way. Also, the convergence behavior of clustering is based on the initial centriod values assigned. This paper focuses on the assignment of cluster centroid selection so as to improve the clustering performance by K-Means clustering algorithm. This paper uses Initial Cluster Centers Derived from Data Partitioning along the Data Axis with the Highest Variance to assign for cluster centroid. Experimental result suggests that the proposed approach results in better clustering result when compared to the conventional technique.


K-Means Clustering, Centroid, Data Partitioning, Variance

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