A Novel Approach to Data Clustering based on Self-Adaptive Bacteria Foraging Optimization

Data clustering reduces the number of data objects by grouping similar data objects together. In this process, data are divided into valuable groups (clusters) or expressive without at all previous information. This manuscript represents a different clustering algorithm based on the technique of the adaptive strategy algorithm known as Self -Adaptive Bacterial Foraging Optimization (SABFO). It is a streamlining strategy for bunching issues where a cluster of bacteria forages to converge to definite locations as ultimate group communities by limiting the fitness function. The superiority of this method is assessed on numerous famous benchmark data sets. In this paper, the authors have compared the projected technique with some wellknown advanced clustering approaches: the k -means algorithm, the Particle Swarm optimization algorithm, and the FitnessBased Adaptive Differential Evolution (FBADE) Scheme. An experimental finding demonstrates the usefulness of the projected algorithm as a clustering method that can operate on data sets with different densities, and cluster sizes.