Hybrid Grey Wolf Optimization - Cheetah Optimization (HGWOCO) algorithm for solving initial cluster center and trade-off issues in Partitional Clustering

A cluster is a group having data objects. The grouping of similar data objects in a cluster based upon some distance function is known as clustering. The data objects within a cluster must be homogenous while the data objects of different clusters must be heterogonous to each other. In this work, a hybrid grey wolf optimization-cheetah optimization algorithm (HGWOCO) is used to address problems pertaining to partitional clustering. The standard partitional clustering algorithm like K-Means stuck in local optimum solutions and face premature convergence. The proposed hybrid version aims at optimization of partitional clustering by addressing the collection of initial cluster centers and trade off issues. The proposed algorithm is validated against some of the well-known datasets. The outcomes of the results reflected that the proposed algorithm is better than the existing ones.