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An Efficient Cluster by Cluster Head Selection Approach in Big Data

Clustering is essential for exploring data, making predictions, and overcoming data anomalies. Reiterative approaches are used to organize clusters in a dataset that have intrinsic, identical properties. Considering the exponential growth of data in the real world, very huge datasets with little or no prior information can be clustered to reveal fascinating patterns. This paper presents Log Centroid-based K-Means (LC-KMeans) clustering techniques in order to optimize the complexity of large data in the Reducer phase and this proposed LCK Means will help to select an efficient Cluster Head using another proposed algorithm Minkowski Distance-based Sea Lion Optimization (MD-SLnO). We compared the number of clusters, the time required to cluster, and the time required to pick a cluster head to existing algorithms.