Fuzzy C-Means Algorithm Implementation with MATLAB Code

This document presents an m-file implementation of the Fuzzy C-Means algorithm accompanied by comprehensive explanations. The Fuzzy C-Means algorithm is a widely-used clustering technique that partitions data points into multiple clusters based on membership degrees. The implementation includes key algorithmic components such as membership matrix initialization, centroid calculation using weighted averages, and iterative optimization of the objective function. Through studying this m-file, researchers can gain deeper insights into the algorithm's principles, including how it handles overlapping clusters and computes fuzzy membership values. The code demonstrates practical implementation aspects like convergence criteria checking, distance metric calculations, and membership updating mechanisms. This resource aims to facilitate better understanding and application of the algorithm in future research projects by providing hands-on coding examples and algorithmic explanations.