🧪 Artificial Intelligence Unsupervised Learning Simulation

This simulation explores unsupervised learning through fuzzy c-means clustering — an algorithm that discovers structure in unlabeled 3D data without ever being told the right answer. Points start with random cluster memberships; each step alternately recomputes the cluster centroids from those memberships and then recomputes the memberships from the new centroids, minimizing a weighted-distance objective until the system converges. Unlike hard clustering, every point keeps a soft membership degree to every cluster, visualized live as a blended color, alongside a fuzzifier control that tunes how crisp or blurry the discovered groups are, and live readouts of the objective function, average crispness, and centroid movement per iteration.