Genetic Optimization Algorithm for Enhanced Fuzzy C-Means Clustering

This research focuses on applying genetic optimization algorithms to enhance fuzzy C-means clustering. Through global adaptive optimization techniques, we aim to identify more precise fuzzy clustering centers by leveraging the powerful search capabilities of genetic algorithms. The implementation involves iterative processes where genetic operators (selection, crossover, and mutation) work on population-based solutions to optimize cluster centroids. Key functions include fitness evaluation using cluster validity indices and chromosome encoding representing cluster centers. This methodology enables continuous iteration and refinement of clustering results, ultimately achieving superior clustering performance. By this approach, we can more accurately determine cluster membership for data points, providing more valuable and reliable clustering outcomes. The primary objective of this study is to enhance the accuracy and reliability of fuzzy clustering algorithms, thereby offering more effective support for data analysis and decision-making in practical applications.