This study tackles the growing complexity of data by presenting a novel approach to KMeans clustering: the Artificial Bee Colony (ABC) and Genetic Algorithms (GA) KMeans algorithm. The traditional K-Means method has inherent weaknesses, such as arbitrary cluster selection and random initialization of cluster centers. This research addresses these issues by focusing on determining the optimal number of clusters in unlabeled data, a key requirement for effective clustering. The proposed ABC_GA_KMeans algorithm overcomes these challenges through autonomous optimization of data collection and cluster centers. It achieves this by integrating ABC optimization and GA to solve the binary optimization problem often encountered in K-Means clustering. Additionally, the inclusion of Genetic Neighbourhood Generators (GNG) enhances the algorithm's ability to compare results within the ABC network, contributing to improved robustness and efficiency. The study conducts extensive experiments with both .....

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