Perform 1D FCM Clustering [v,U]=Cluster1D(m,x,MaxClusterNo,DimNo,options) ------------------------------------------------------------------------- Parameters ------------------------------------------------------------------------- x - data points column vector. The values should be sorted in ascending order 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 ------------------------------------------------------------------------- Remarks ------------------------------------------------------------------------- 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.