Text document clustering plays a pivotal role in organizing large-scale unstructured data, yet conventional clustering algorithms, such as k-means, often struggle with high-dimensional data, suboptimal initializations, and local minima issues. This paper introduces a novel comparative analysis of three advanced optimization techniques integrated with k-means: Chaotic Northern Goshawk Optimization (CNGO), Mayfly Optimization Algorithm (MOA), and Modified Particle Swarm Optimization (MPSO). This work is unique because it integrates these metaheuristic algorithms to improve clustering performance, targeting initialization challenges and increasing accuracy. Extensive experiments were conducted on benchmark datasets, including Reuters-21578, 20-Newsgroup, and BBC-Sport. All three models outper- form traditional k-means in terms of accuracy, Adjusted Rand Index (ARI), Normalized Mutual Information (NMI), and V-measure.This research offers new insights into optimizing clustering processes using metaheuristic algorithms and provides a foundation for future exploration in large-scale document clustering.
Our study is significant because it systematically overcomes key limitations of conventional k-means for high-dimensional text data poor centroid initialization, local minima, and reduced effectiveness on sparse corpora by integrating and comparatively evaluating three advanced metaheuristics (CNGO, MOA, MPSO) with k-means on standard benchmark datasets. The value of this work lies in the consistently improved clustering quality (Accuracy, ARI, NMI, V-measure) achieved by the proposed hybrids, and in showing that MOA–k-means in particular offers a robust, scalable solution for real-world text analytics applications such as information retrieval, recommendation, and topic discovery.