MATLAB Implementation of Fuzzy Clustering Algorithm

The article presents a fuzzy clustering program accompanied by detailed documentation that outlines each step of the fuzzy clustering algorithm. The primary objective of this program is to partition dataset elements into distinct groups through a series of computational and analytical procedures, thereby facilitating better data understanding and processing. The fuzzy clustering algorithm involves multiple critical steps including initialization of the membership matrix, calculation of cluster centroids, and iterative updates of the membership matrix. In the code implementation, the membership matrix initialization typically uses random assignment or distance-based methods, while centroid calculation employs weighted averaging based on membership degrees. The program executes these steps through iterative optimization loops, often incorporating convergence criteria checks using matrix norm comparisons between iterations. By executing these algorithmic steps, the fuzzy clustering program effectively identifies similarities and relationships within data, providing valuable insights for subsequent data analysis and decision-making processes.