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Perform 1D FCM Clustering


function [v,U]=Cluster1D(x,MaxClusterNo,DimNo,options)


 Perform 1D FCM Clustering


 x            - data points column vector. The values should be sorted in ascending
 MaxClusterNo - the maximum number of clusters
 DimNo        - the number of the current 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: 7)
   options(15): number of decimals (default: 2)
   options(16): initialization mode of the memebership matrix by
                FCM (default: 0('deterministic'))
   options(17): treshold for the examination of the identity of two
                cluster centers (default:1e-6)
 Returned values
 v - column vector containing the cluster centers
 U - matrix containing the membership values
 The optimal number of clusters is determined by the help of the FS
 Fukuyama-Sugeno index.

 Zsolt Csaba Johanyák, johanyak.csaba@gamf.kefo.hu, v. 1.3, 31. August 2007.


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