FCM算法的matlab程序

1.采用iris数据库

iris_data.txt

5.1 3.5 1.4 0.2

4.9 3 1.4 0.2

4.7 3.2 1.3 0.2

4.6 3.1 1.5 0.2

5 3.6 1.4 0.2

5.4 3.9 1.7 0.4

4.6 3.4 1.4 0.3

5 3.4 1.5 0.2

4.4 2.9 1.4 0.2

4.9 3.1 1.5 0.1

5.4 3.7 1.5 0.2

4.8 3.4 1.6 0.2

4.8 3 1.4 0.1

4.3 3 1.1 0.1

5.8 4 1.2 0.2

5.7 4.4 1.5 0.4

5.4 3.9 1.3 0.4

5.1 3.5 1.4 0.3

5.7 3.8 1.7 0.3

5.1 3.8 1.5 0.3

5.4 3.4 1.7 0.2

5.1 3.7 1.5 0.4

4.6 3.6 1 0.2

5.1 3.3 1.7 0.5

4.8 3.4 1.9 0.2

5 3 1.6 0.2

5 3.4 1.6 0.4

5.2 3.5 1.5 0.2

5.2 3.4 1.4 0.2

4.7 3.2 1.6 0.2

4.8 3.1 1.6 0.2

5.4 3.4 1.5 0.4

5.2 4.1 1.5 0.1

5.5 4.2 1.4 0.2

4.9 3.1 1.5 0.2

5 3.2 1.2 0.2

5.5 3.5 1.3 0.2

4.9 3.6 1.4 0.1

4.4 3 1.3 0.2

5.1 3.4 1.5 0.2

5 3.5 1.3 0.3

4.5 2.3 1.3 0.3

4.4 3.2 1.3 0.2

5 3.5 1.6 0.6

5.1 3.8 1.9 0.4

4.8 3 1.4 0.3

5.1 3.8 1.6 0.2

4.6 3.2 1.4 0.2

5.3 3.7 1.5 0.2

5 3.3 1.4 0.2

7 3.2 4.7 1.4

6.4 3.2 4.5 1.5

6.9 3.1 4.9 1.5

5.5 2.3 4 1.3

6.5 2.8 4.6 1.5

5.7 2.8 4.5 1.3

6.3 3.3 4.7 1.6

4.9 2.4 3.3 1

6.6 2.9 4.6 1.3

5.2 2.7 3.9 1.4

5 2 3.5 1

5.9 3 4.2 1.5

6 2.2 4 1

6.1 2.9 4.7 1.4

5.6 2.9 3.6 1.3

6.7 3.1 4.4 1.4

5.6 3 4.5 1.5

5.8 2.7 4.1 1

6.2 2.2 4.5 1.5

5.6 2.5 3.9 1.1

5.9 3.2 4.8 1.8

6.1 2.8 4 1.3

6.3 2.5 4.9 1.5

6.1 2.8 4.7 1.2

6.4 2.9 4.3 1.3

6.6 3 4.4 1.4

6.8 2.8 4.8 1.4

6.7 3 5 1.7

6 2.9 4.5 1.5

5.7 2.6 3.5 1

5.5 2.4 3.8 1.1

5.5 2.4 3.7 1

5.8 2.7 3.9 1.2

6 2.7 5.1 1.6

5.4 3 4.5 1.5

6 3.4 4.5 1.6

6.7 3.1 4.7 1.5

6.3 2.3 4.4 1.3

5.6 3 4.1 1.3

5.5 2.5 4 1.3

5.5 2.6 4.4 1.2

6.1 3 4.6 1.4

5.8 2.6 4 1.2

5 2.3 3.3 1

5.6 2.7 4.2 1.3

5.7 3 4.2 1.2

5.7 2.9 4.2 1.3

6.2 2.9 4.3 1.3

5.1 2.5 3 1.1

5.7 2.8 4.1 1.3

6.3 3.3 6 2.5

5.8 2.7 5.1 1.9

7.1 3 5.9 2.1

6.3 2.9 5.6 1.8

6.5 3 5.8 2.2

7.6 3 6.6 2.1

4.9 2.5 4.5 1.7

7.3 2.9 6.3 1.8

6.7 2.5 5.8 1.8

7.2 3.6 6.1 2.5

6.5 3.2 5.1 2

6.4 2.7 5.3 1.9

6.8 3 5.5 2.1

5.7 2.5 5 2

5.8 2.8 5.1 2.4

6.4 3.2 5.3 2.3

6.5 3 5.5 1.8

7.7 3.8 6.7 2.2

7.7 2.6 6.9 2.3

6 2.2 5 1.5

6.9 3.2 5.7 2.3

5.6 2.8 4.9 2

7.7 2.8 6.7 2

6.3 2.7 4.9 1.8

6.7 3.3 5.7 2.1

7.2 3.2 6 1.8

6.2 2.8 4.8 1.8

6.1 3 4.9 1.8

6.4 2.8 5.6 2.1

7.2 3 5.8 1.6

7.4 2.8 6.1 1.9

7.9 3.8 6.4 2

6.4 2.8 5.6 2.2

6.3 2.8 5.1 1.5

6.1 2.6 5.6 1.4

7.7 3 6.1 2.3

6.3 3.4 5.6 2.4

6.4 3.1 5.5 1.8

6 3 4.8 1.8

6.9 3.1 5.4 2.1

6.7 3.1 5.6 2.4

6.9 3.1 5.1 2.3

5.8 2.7 5.1 1.9

6.8 3.2 5.9 2.3

6.7 3.3 5.7 2.5

6.7 3 5.2 2.3

6.3 2.5 5 1.9

6.5 3 5.2 2

6.2 3.4 5.4 2.3

5.9 3 5.1 1.8

View Code

2.matlab源程序

function label_1=My_FCM(K)

%输入K：聚类数

%输出：label_1:聚的类, para_miu_new:模糊聚类中心μ，responsivity:模糊隶属度

format long

eps=1e-5; %定义迭代终止条件的eps

alpha=2; %模糊加权指数，[1,+无穷)

max_iter=100; %最大迭代次数

fitness=zeros(max_iter,1);

data=dlmread('E:\www.cnblogs.comkailugaji\data\iris\iris_data.txt');

%----------------------------------------------------------------------------------------------------

%对data做最大-最小归一化处理

[data_num,~]=size(data);

X=(data-ones(data_num,1)*min(data))./(ones(data_num,1)*(max(data)-min(data)));

[X_num,X_dim]=size(X);

%----------------------------------------------------------------------------------------------------

%随机初始化模糊隶属度矩阵

responsivity=rand(X_num,K); %初始化模糊隶属度矩阵，X_num*K

temp=sum(responsivity,2); %把responsivity每一行加起来，把K类加起来，N*1的矩阵

responsivity=responsivity./(temp*ones(1,K)); %保证每行(每类)加起来为1

% ----------------------------------------------------------------------------------------------------

% FCM算法

for t=1:max_iter

%更新聚类中心K*X_dim

miu_up=(responsivity'.^(alpha))*X; %μ的分子部分

para_miu=miu_up./((sum(responsivity.^(alpha)))'*ones(1,X_dim));

%欧氏距离，计算(X-para_miu)^2=X^2+para_miu^2-2*para_miu*X'，矩阵大小为X_num*K

distant=(sum(X.*X,2))*ones(1,K)+ones(X_num,1)*(sum(para_miu.*para_miu,2))'-2*X*para_miu';

%目标函数值

fitness(t)=sum(sum(distant.*(responsivity.^(alpha))));

%更新隶属度矩阵X_num*K

R_up=distant.^(-1/(alpha-1)); %隶属度矩阵的分子部分

responsivity=R_up./(sum(R_up,2)*ones(1,K));

%[responsivity,para_miu,fitness(t)]=FuzzyCM(X,responsivity,K,alpha);

if t>1 %改成while不行

if abs(fitness(t)-fitness(t-1))

break;

end

end

end

%iter=t; %实际迭代次数

[~,label_1]=max(responsivity,[],2);

3.结果

>> label_1=My_FCM(3)

label_1 =

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

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1

1

1

1

1

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1

1

1

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3

3

2

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3

3

3

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3

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3

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3

3

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3

3

3

3

3

3

3

2

3

3

3

3

3

3

3

3

3

3

3

3

3

2

3

2

2

2

2

3

2

2

2

2

2

2

3

2

2

2

2

2

3

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3

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2

2

3

4.注意

由于初始化模糊隶属度矩阵是随机的，所以每次出现的结果并不一样，如果答案与上述不一致，很正常，可以设置迭代次数，求精度。如有不对之处，望指正。