A Modified Grey Wolf Optimizer Based Data Clustering Algorithm

Data clustering is an important data analysis and data mining tool in many fields such as pattern recognition and image processing. The goal of data clustering is to optimally organize similar objects into clusters. Grey wolf optimizer is a newly introduced optimization algorithm with inspiration from the social behavior of gray wolves. In this work, we propose a modified gray wolf optimizer to tackle some of the challenges in meta-heuristic algorithms. These modifications include a balanced approach to the exploration and exploitation stages of the algorithm as well as a local search around the best solution found. The performance of the proposed algorithm is compared to seven other clustering methods on nine data sets from the UCI machine learning laboratory. Experimental results demonstrate the competence of the proposed algorithm in solving data clustering problems. Overall, the intra-cluster distance of the proposed algorithm is lower than other algorithms and gives an average error rate of 11.22% which is the lowest among all.

Automated summary

The study proposed a modified grey wolf optimizer to address the challenges in data clustering, performing well in experiments. The algorithm outperformed seven other clustering methods on nine datasets, with lower intra-cluster distances and the lowest average error rate.