K-Means Hybridization with Enhanced Firefly Algorithm for High-Dimension Automatic Clustering

Abstract

K-means clustering is not able to select the right number of clusters of data items having high-dimension. So, for determining the ideal number of clusters, we have combined PCA with the Silhouette and Elbow approaches. Additionally, we have a large number of meta-heuristic swarm intelligence algorithms which is influenced by nature and were previously used to solve the automatic data clustering problem. Firefly offers reliable and effective automatic data clustering. The Firefly algorithm automatically divides the entire population into subpopulations, which slows down the convergence and reduces the likelihood of capturing local minima in high-dimensional optimization problems. Thus, for automatic clustering, we demonstrated an improved firefly, i.e., we offered a hybridized K-means with an ODFA model. The experimental section displays the results and graphs for the Silhouette, Elbow, and Firefly algorithms.