FCM Fuzzy C-Means Clustering Algorithm with Interactive Implementation

In this article, I would like to introduce an enhanced implementation of the Fuzzy C-Means clustering algorithm, called the FCM Fuzzy C-Means Clustering Algorithm. This version features interactive capabilities through a graphical user interface. The algorithm workflow is straightforward: users first define a rectangular region using mouse selection, where the system captures pixel coordinates and converts them to data space coordinates. The FCM algorithm then automatically processes the selected target region. The implementation typically involves initializing cluster centroids, computing membership values using Euclidean distance metrics, and iteratively updating centroids until convergence criteria are met. This design aims to simplify cluster analysis for users while providing an engaging experience through visual interaction. The core algorithm utilizes fuzzy partitioning where data points can belong to multiple clusters with varying degrees of membership, calculated through weighted exponent parameters. Let's begin exploring this algorithm! Enjoy the process!