performs fuzzy c-means clustering.
[Centers, Memberships] = FuzzyCMeans(Samples, InitialCenters, NormType, Criterion, Value, Blending) [Centers, Memberships] = FuzzyCMeans(Samples, InitialCenters, NormType, Criterion, Value) [Centers] = FuzzyCMeans(Samples, InitialCenters, NormType, Criterion, Value, Blending) [Centers, Memberships] = FuzzyCMeans(Samples, InitialCenters, NormType, Criterion, Value)
matrix that includes sample vectors as rows
matrix that includes initial cluster centers as rows
norm of vectors, must be a scalar greater than zeros, can be %inf
name of termination condition, can be 'iterations' or 'quantization_error'
number of iterations or maximum quantization error
exponent applied to memberships. This parameter is optional. The default value is two.
resulting cluster centers
membership values of the samples (rows) in the clusters (columns). This parameter is optional.
This function performs fuzzy c-means clustering. For each row of Samples the membership in each cluster given by the corresponding row of InitialCenters is calculated. Then the cluster centers are re-calculated.
The process terminates if the specified number of iterations is performed or the fuzzy quantization error does not exceed a specified maximum.
If the termination condition is fullfilled from the beginning, Centers == InitialCenters
Richard O. Duda, Peter E. Hart, David G. Stork, 'Pattern Classification', John Wiley & Sons Inc., New York, Chichester, Weinheim, Brisbane, Singapore, Toronto, Second Edition, 2001