Classic FCM Algorithm Implementation

The Fuzzy C-Means (FCM) clustering algorithm has been implemented as a complete MATLAB function package. This implementation features parameterized design where users only need to provide the input data matrix, number of clusters, and fuzzification parameter. The function includes built-in error checking for input validation to prevent common execution errors. For optimal usage, consider adding descriptive comments regarding your dataset characteristics and expected cluster outcomes. The algorithm employs iterative optimization using membership matrices and cluster centroids, with convergence criteria controlled through maximum iteration count and epsilon tolerance parameters. Before integration into larger projects, thoroughly test edge cases including outlier detection, empty clusters, and varying data dimensionality. The packaged implementation handles distance metric calculations and membership updates automatically, ensuring stable performance across different dataset scales.