Abstract: Clustering of data in case of data mining has a major role in recent research as well as data engineers. It supports for classification and regression type of problems. It needs to obtain the optimized clusters for such application. The partitional clustering and meta-heuristic search techniques are two helpful tools for this task. However the convergence rate is one of the important factors at the time of optimization. In this paper, authors have taken a data clustering approach with improved bee colony algorithm and opposition based learning to improve the rate of convergence and quality of clustering. It introduces the opposite bees that are created using opposition based learning to achieve better exploration. These opposite bees occupy exactly the opposite position that of the mainstream bees in the solution space. Both the mainstream and opposite bees explore the solution space together with the help of Bee Colony Optimization based clustering algorithm. This boosts the explorative power of the algorithm and hence the convergence rate. The algorithm uses a steady state selection procedure as a tool for exploration. The crossover and mutation operation is used to get balanced exploitations. This enables the algorithm to avoid sticking in local optima. To justify the effectiveness of the algorithm it is verified with the open datasets from the UCI machine learning repository as the benchmark. The simulation result shows that it performs better than some benchmark as well as recently proposed algorithms in terms of convergence rate, clustering quality, and exploration and exploitation capability.

Srikanta Kumar Sahoo, Priyabrata Pattanaik, Mihir Narayan Mohanty and Dilip Kumar Mishra, “Opposition Learning Based Improved Bee Colony Optimization (OLIBCO) Algorithm for Data Clustering” International Journal of Advanced Computer Science and Applications(IJACSA), 14(4), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140429

@article{Sahoo2023,
title = {Opposition Learning Based Improved Bee Colony Optimization (OLIBCO) Algorithm for Data Clustering},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140429},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140429},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {4},
author = {Srikanta Kumar Sahoo and Priyabrata Pattanaik and Mihir Narayan Mohanty and Dilip Kumar Mishra}
}