Automatic Data Clustering using Dynamic Crow Search Algorithm

Abstract

This work proposes Automatic clustering using Dynamic Crow Search Algorithm, which updates its parameters dynamically. Crow Search is a recently proposed algorithm that imitates the working of crow. Clustering is an essential aspect of data analysis whose significance has increased manifold since the advancements of technology which has led to enormous data generation, which need to be analysed in real-time. Automatic clustering detects optimal cluster numbers and produces sustainable cluster centroids. ACDCSA uses Cluster Validity using Nearest Neighbour as an internal validity measure that acts as a fitness function to find the optimal cluster centres. The present work is compared with some well-known other meta-heuristic search algorithms like PSO, DE, WOA and GWO for the automatic clustering task over seven benchmark clustering datasets. Inter-cluster distance, intra-cluster distance and the optimal cluster number produced are used to assess the performance of ACDCSA.