This study presents the K-means clustering-based grey wolf optimizer, a new algorithm
intended to improve the optimization capabilities of the conventional grey wolf optimizer in …

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 …

Grey wolf optimizer (GWO) is known as one of the recent popular metaheuristic algorithms
inspired from the social collaboration and team hunting activities of grey wolves in nature …

K-means clustering algorithm is a partitional clustering algorithm that has been used widely
in many applications for traditional clustering due to its simplicity and low computational …

The application of several swarm intelligence and evolutionary metaheuristic algorithms in
data clustering problems has in the past few decades gained wide popularity and …

In cluster analysis, the goal has always been to extemporize the best possible means of
automatically determining the number of clusters. However, because of lack of prior domain …

The firefly algorithm is a nature-inspired metaheuristic optimization algorithm that has
become an important tool for solving most of the toughest optimization problems in almost all …

One heuristic evolutionary algorithm recently proposed is the grey wolf optimizer (GWO),
inspired by the leadership hierarchy and hunting mechanism of grey wolves in nature. This …

Data present in abundance increases the complexity of handling them, which affects the
effective decision-making process. Hence, data clustering gains remarkable importance in …

Clustering is an unsupervised data analytic technique that can determine the similarity
between data objects and put the similar data objects into one cluster. The similarity among …