MOCF: A Multi-Objective Clustering Framework using an Improved Particle Swarm Optimization Algorithm

Traditional clustering algorithms, such as K-Means, perform with a single goal in mind. However, many real-world applications, multiple objective functions must be considered at the same time. Furthermore, traditional algorithms have drawbacks centroid selection, local optimal, and convergence. Particle Swarm Optimization (PSO)-based approaches were developed to address these shortcomings. Animals their social Behaviour, particularly bird flocking fish schooling, inspire PSO. This paper proposes Multi-Objective Clustering Framework (MOCF), an improved PSO-based framework. As algorithm, (PSO) based (PSO-MOC) is proposed. It significantly improves efficiency. The proposed framework's performance evaluated using variety of datasets. To test prototype application was built Python data science platform. empirical results showed that multi-objective outperformed its single-objective counterparts.