Fuzzy C-Means (FCM)
- AI Solver
- FCM: The fuzzy c-means clustering algorithm | J. Bezdek, R. Ehrlich, and W. Full - ScienceDirect
One of the most widely used fuzzy clustering algorithms is the Fuzzy C-means clustering (FCM) Algorithm. Fuzzy logic principles can be used to cluster multidimensional data, assigning each point a membership in each cluster center from 0 to 100 percent. This can be very powerful compared to traditional hard-thresholded clustering where every point is assigned a crisp, exact label.
The fuzzy c-means algorithm is very similar to the k-means algorithm:
- Choose a number of clusters.
- Assign coefficients randomly to each data point for being in the clusters.
- Repeat until the algorithm has converged (that is, the coefficients' change between two iterations is no more than E, the given sensitivity threshold) :
- Compute the centroid for each cluster.
- For each data point, compute its coefficients of being in the clusters.