MATLAB Implementation of Localized Fuzzy C-Means Clustering Algorithm

This is a MATLAB code implementation example for the Localized Fuzzy C-Means clustering algorithm. The algorithm effectively handles data ambiguity by incorporating localized membership functions and distance metrics to improve clustering accuracy for complex datasets. The implementation demonstrates how to structure the MATLAB code with proper initialization of cluster centers, iterative membership updates using fuzzy partitioning, and localized neighborhood considerations. Key functions include data normalization, distance matrix computation with localization parameters, and convergence criteria checking. Each code section contains detailed comments explaining the algorithmic logic, such as handling the fuzzy partition matrix update with exponential weighting and implementing the localized distance adaptation mechanism. Through this comprehensive example with practical code explanations, users can understand how to integrate localized fuzzy clustering into their projects, enhancing data processing accuracy and analytical efficiency while managing computational complexity through optimized iteration controls.