A Hybrid K-Means++ and Particle Swarm Optimization Approach for Enhanced Document Clustering
Eisha Hassan; Fazila‐Tun‐Nesa Malik; Qazi Waqas Khan; Nadeem Ahmad; Muhammad Sardaraz; Faten Khalid Karim; Hela Elmannai · 2025 · IEEE Access
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Abstract
Document Clustering has attracted the interest of many researchers who have created several solutions to this problem by combining different techniques, models, and algorithms. While famous for its simplicity, the most commonly used algorithm, K-Means, suffers from issues such as finding the optimal value for k and random initialization of the centroids. In this paper, we propose a hybrid methodology combining K-Means++ with the metaheuristic algorithm PSO to overcome the challenges of both these algorithms. K-Means++ is a smart initialization technique that selects clusters based on probabili
Abstract by Eisha Hassan; Fazila‐Tun‐Nesa Malik; Qazi Waqas Khan; Nadeem Ahmad; Muhammad Sardaraz; Faten Khalid Karim; Hela Elmannai, IEEE Access (2025) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1109/access.2025.3535226