fcm.rar_FCM聚类_fcm_fcm 分类 matlab_模糊分类_模糊聚类

function [center, U, obj_fcn] = fcm(data, cluster_n, options) %FCM Data set clustering using fuzzy c-means clustering. % % [CENTER, U, OBJ_FCN] = FCM(DATA, N_CLUSTER) finds N_CLUSTER number of % clusters in the data set DATA. DATA is size M-by-N, where M is the number of % data points and N is the number of coordinates for each data point. The % coordinates for each cluster center are returned in the rows of the matrix % CENTER. The membership function matrix U contains the grade of membership of % each DATA point in each cluster. The values 0 and 1 indicate no membership % and full membership respectively. Grades between 0 and 1 indicate that the % data point has partial membership in a cluster. At each iteration, an % objective function is minimized to find the best location for the clusters % and its values are returned in OBJ_FCN. % % [CENTER, ...] = FCM(DATA,N_CLUSTER,OPTIONS) specifies a vector of options % for the clustering process: % OPTIONS(1): exponent for the 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: 1) % The clustering process stops when the maximum number of iterations % is reached, or when the objective function improvement between two % consecutive iterations is less than the minimum amount of improvement % specified. Use NaN to select the default value. % % Example % data = rand(100,2); % [center,U,obj_fcn] = fcm(data,2); % plot(data(:,1), data(:,2),'o'); % hold on; % maxU = max(U); % % Find the data points with highest grade of membership in cluster 1 % index1 = find(U(1,:) == maxU); % % Find the data points with highest grade of membership in cluster 2 % index2 = find(U(2,:) == maxU); % line(data(index1,1),data(index1,2),'marker','*','color','g'); % line(data(index2,1),data(index2,2),'marker','*','color','r'); % % Plot the cluster centers % plot([center([1 2],1)],[center([1 2],2)],'*','color','k') % hold off; % % See also FCMDEMO, INITFCM, IRISFCM, DISTFCM, STEPFCM. % Roger Jang, 12-13-94, N. Hickey 04-16-01 % Copyright 1994-2002 The MathWorks, Inc. % $Revision: 1.13 $ $Date: 2002/04/14 22:20:38 $ if nargin ~= 2 & nargin ~= 3, error('Too many or too few input arguments!'); end data_n = size(data, 1); in_n = size(data, 2); % Change the following to set default options default_options = [2; % exponent for the partition matrix U 100; % max. number of iteration 1e-5; % min. amount of improvement 1]; % info display during iteration if nargin == 2, options = default_options; else % If "options" is not fully specified, pad it with default values. if length(options) < 4, tmp = default_options; tmp(1:length(options)) = options; options = tmp; end % If some entries of "options" are nan's, replace them with defaults. nan_index = find(isnan(options)==1); options(nan_index) = default_options(nan_index); if options(1) <= 1, error('The exponent should be greater than 1!'); end end expo = options(1); % Exponent for U max_iter = options(2); % Max. iteration min_impro = options(3); % Min. improvement display = options(4); % Display info or not obj_fcn = zeros(max_iter, 1); % Array for objective function U = initfcm(cluster_n, data_n); % Initial fuzzy partition % Main loop for i = 1:max_iter, [U, center, obj_fcn(i)] = stepfcm(data, U, cluster_n, expo); if display, fprintf('Iteration count = %d, obj. fcn = %f\n', i, obj_fcn(i)); end % check termination condition if i > 1, if abs(obj_fcn(i) - obj_fcn(i-1)) < min_impro, break; end, end end iter_n = i; % Actual number of iterations obj_fcn(iter_n+1:max_iter) = [];