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K-means Clustering is the most well-known partitioning algorithm among allclustering, by which we can partition the data objects very easily in to morethan one clusters. However, for K-means to choose an appropriate number ofclusters without any prior domain knowledge about the dataset is challenging,especially in high-dimensional data objects. Hence, we have implemented theSilhouette and Elbow methods with PCA to find an optimal number of clusters.Also, previously, so many meta-heuristic swarm intelligence algorithms inspiredby nature have been employed to handle the automatic data clustering problem.Firefly is efficient and robust for automatic clustering. However, in theFirefly algorithm, the entire population is automatically subdivided intosub-populations that decrease the convergence rate speed and trapping to localminima in high-dimensional optimization problems. Thus, our study proposed anenhanced firefly, i.e., a hybridized K-means with an ODFA model for automaticclustering. The experimental part shows output and graphs of the Silhouette andElbow methods as well as the Firefly algorithm

Hybridization of K-means with improved firefly algorithm for automatic clustering in high dimension