A Survey on Recent Advances in Multi-objective Evolutionary Clustering

Clustering is a core method in data mining and machine learning that focuses on dividing data into logically consistent and interpretable groups. While traditional clustering methods typically optimize a single-objective function, many real-world problems necessitate the simultaneous consideration of multiple, often conflicting, objectives, such as compactness, separation, and interpretability. Multi-objective clustering addresses this complexity by leveraging evolutionary algorithms and heuristic optimization strategies to generate a diverse set of Pareto-optimal solutions. This chapter presents a comprehensive overview of recent developments in multi-objective clustering, delving into state-of-the-art optimization methods, and challenges in the field. It also examines practical applications across various domains, including image processing, bioinformatics, software engineering, and social network analysis, illustrating the growing relevance and impact of multi-objective approaches in real-world scenarios.