Perform Fuzzy C-Mean Clustering in each indata dimension separately
(1D clusterings)
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Parameters
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xtrain - matrix containing the training data points
Each row corresponds to a data point, each column corresponds to a
dimension.
indata(i) --name
|-range
LastMfNumbers - row vector containing the ordinal numbers of the
membership functions for each dimension
options - column vector containing parameters of the FCM clustering
options(1): exponent for the partition matrix U (default: 2.0)
options(2): maximum number of iterations (default: 100)
options(3): minimum amount of improvement (default: 1e-5)
options(4): info display during iteration (default: 0)
options(5): FS index plotting (default: 0)
options(6): indicates whether cluster merging should be done (default: 1)
options(7): indicates whether the clusters should be plotted (default: 0)
options(8): indicates whether the approximated trapezoids should be
plotted (default: 0)
options(9): indicates whether the number of clusters can be reduced to
1 during cluster merging (default: 0)
options(10): similarity treshold for set merging (default: 0.05)
options(11): fuzzy index for the calculation of the FS index
(default: 2)
options(12): cut level for the approximation of the trapezoids
(default: 0.85)
options(13): info display during optimal cluster number determination
(default: 0)
options(14): maximum number of clusters (default: 3)
options(15): number of decimals (default: 2)
options(16): initialization mode of the membership matrix by
FCM (default: 0('deterministic'))
options(17): treshold for the examination of the identity of two
cluster centers (default:1e-6)
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Returned values
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Vector containing the structures that decribe linguistic variables
input(i) --name
|-range
|-mf(j)--name
|-RPx
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Remarks
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Zsolt Csaba Johanyák, johanyak.csaba@gamf.kefo.hu, v. 1.2, 23. February 2007.