Fuzzy C-Means Clustering Algorithm Source Code

This repository provides the original MATLAB implementation of the Fuzzy C-Means (FCM) clustering algorithm. The FCM algorithm is a clustering technique based on fuzzy set theory that partitions data samples into different clusters while measuring membership degrees between samples and clusters through fuzzy similarity metrics. The implementation utilizes MATLAB's matrix operations and iterative computation capabilities to execute the algorithm efficiently. The code includes comprehensive definitions of variables and parameters such as cluster centers, membership matrices, and fuzzification exponents, along with iterative update processes for centroid calculation and membership value optimization. Key functions handle data normalization, distance computation using Euclidean metrics, and convergence checking through tolerance thresholds. By employing this algorithm, researchers can effectively analyze sample similarities and differences, classify data into appropriate clusters, and enhance data analysis and mining capabilities. The code structure follows modular programming principles with clear separation between initialization, main iteration loop, and results output sections.