Enhanced C-Means Clustering Algorithm

In previous coursework, we studied an algorithm known as "c-means clustering," which serves to partition data points into distinct groups. This implementation utilizes the standard k-means approach with centroid-based clustering methodology, where data points are assigned to the nearest cluster center based on Euclidean distance calculations. At that time, we employed older versions of MATLAB software that supported this algorithm through functions like kmeans() in the Statistics and Machine Learning Toolbox. The algorithm typically involves iterative centroid updates and point reassignments until convergence criteria are met. However, with the current availability of newer MATLAB versions, we have not yet attempted to implement this algorithm in the updated environment. While untested in recent MATLAB releases, we anticipate investigating its performance characteristics in modern versions, particularly regarding potential enhancements in computational efficiency, handling of large datasets, and integration with newer machine learning workflows. The algorithm's core functionality likely remains accessible through MATLAB's updated clustering functions, though parameter specifications and optimization techniques may require adjustments for optimal performance.