EHD-ABC: An Enhanced History-Driven Artificial Bee Colony Algorithm for Improved Data Clustering

Data clustering is a critical data mining technique for grouping similar objects and differentiating dissimilar ones. While advancements in machine learning, statistical, and metaheuristic methods have addressed some challenges, issues like accuracy, efficiency, and scalability persist. Building on the History-Based Artificial Bee Colony (HD-ABC) algorithm, this paper introduces the Enhanced History-Driven Artificial Bee Colony (EHD-ABC) algorithm. Refining the historical memory mechanism and optimization process, the proposed algorithm achieves improved clustering accuracy, reduced computational complexity, and enhanced efficiency. Experimental results on artificial and real-world datasets demonstrate EHD-ABC's superiority over existing methods in clustering quality and error reduction, such as HD-ABC and K-means.