刚开始用matlab学C均值聚类算法,在网上找了好久发现东西很散很离乱，说的不清楚，下面就这个问题做详细的讲解，力求看过大家都能明白，这个算法比较简单，但是很常用，FCM代码的全解，以及用在FCM进行图像分割，希望大家看过后给我投票，或是有什么问题不懂的可以在下面给我留言工具/原料morewin7.8 64位+matlab12b方法/步骤1/7分步阅读

首先，你要知道什么是C均值聚类算法，就是那个公式，你最霜吐好要能推出来，其次，要明白matlab中自带FCM 的代码含义，在命令窗中输入 edit fcm; 会在M文爷晃场件中打开，前面是注释

function = fcm(data, cluster_n, options)

%FCM Data set clustering using fuzzy c-means clustering.

%% = 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.%

% = 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歌冲 (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);

% = 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(,1)],,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 $

% %后是说明部分，从此处开始是函数定义

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 = ;% 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 nans, 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,

= 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) = ;

英文看起来比较郁闷的看中文如下

2/7

关于初始化子函数function U = initfcm(cluster_n, data_n)， 代码全解如下

3/7

下一个迭代子函数 function = stepfcm(data, U, cluster_n, expo)

4/7

下一个计算距离子函数 function out = distfcm(center, data)

5/7

所以这些函数都是用matlab 自带的函数，包括子函数，你可以把所有的函数放在一个M文件中 下面将贴出我自己的关于FCM的全码，都是在自带函数基础上改的，

6/7

接着进行图像分割，调用代码如下，可以直接输入在命令窗口中，这段代码大家要好好研究

7/7

下面展示下效果图，有迭代次数，聚类中心，还有分割后的图像。大家研究下吧

注意事项

转载引用注明出处，欢迎投票，欢迎留言！

matlab