Clustering is helpful in different areas of interdisciplinary engineering. It helps in finding the alike element in a single label. The clustering efficiency depends on the centroid calculation and the nearest distance estimation. This paper's main aim is to review and analysis the method in finding the better clustering mechanism to extract the higher efficiency.In this regard different methods from the previous approaches have been discussed and their advantages have been highlighted. Based on the identified gaps, future suggestions have been listed for the efficient clustering mechanism.

An iterative centroid initialization k-means (ICKM) based clustering has been proposed in this paper. In this approach first the dataset selection has been performed along with the option of choosing and selection as per the data use or the user can access partial data also based on the iterative centroid.Then the data preprocessing steps are followed for the data arrangement and analysis. There are four different distance algorithms have been considered with the k-means. These algorithms provide the complete variability for the distance estimation and production. The proposed method found to be useful along with different distance estimation and measures.