Data cluster, an allocation of contiguous storage in databases and file systems. K-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. K-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells. Clustering of data is commonly employed in all the data mining applications. The consensus clustering refers to the clustering of the partitioned clusters based on the optimal functions. Convex functions, Utility functions and distance metric functions were employed for the clustering of the input data. The input data is initially partitioned and the partitioned data is then clustered efficiently based on the utility functions defined. The basic portioning is based on K-means clustering process. For the obtained partitions the utility functions and the convex functions were calculated and the data is divided into clusters. The clustering quality of the proposed method is much improved compared to the existing works. The process is to cluster the input data. And to partition the input data and employ the consensus clustering to the input data. And also to calculate the Coefficient of Variation for the resulting clusters into proves the efficiency of the clustering.