11-08-2012, 12:40 PM
Fuzzy C-Means Clustering
Fuzzy C-Means.ppt (Size: 653 KB / Downloads: 68)
Initial Choices
Number of clusters
Maximum number of iterations (Typ.: 100)
Weighting exponent (Fuzziness degree)
m=1: crisp
m=2: Typical
Termination measure 1-norm
Termination threshold (Typ. 0.01)
Pros and Cons
Advantages
Unsupervised
Always converges
Disadvantages
Long computational time
Sensitivity to the initial guess (speed, local minima)
Sensitivity to noise
One expects low (or even no) membership degree for outliers (noisy points)