when dealing with soft and hard clustering techniques such as K-means and fuzzy C-means I run into abit of difficulty on the steps that FCM takes to calculate the clusters.
For instance in K-means the steps are as follows:
- Chose number of clusters (K)
- Initialize centroids (K patterns randomly chosen from data set)
- Assign each pattern to the cluster with closest centroid
- Calculate means of each cluster to be its new centroid
- Repeat step 3 until a stopping criteria is met (no pattern move to another cluster)
Could some one explain the steps of fuzzy C-means?