-
Notifications
You must be signed in to change notification settings - Fork 1
/
kmedianCluster.m
80 lines (65 loc) · 1.38 KB
/
kmedianCluster.m
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
function [ idx, mu, itr ] = kmedianCluster( varargin )
if nargin == 2
dataSet = varargin{1};
k = varargin{2};
else
return;
end
%Initialize variables
x = dataSet(:,:)';
p = size(x,1);
n = size(x,2);
g = k;
Q = 0;
tau = zeros(g,n);
D = zeros(g,n);
%Initialize parameters
median = randperm(n,g);
count = 0;
for itr = 1:200
%E-step
for i = 1:g
D(i,:) = sum((x - x(:,median(1,i))*ones(1,n)).^2);
end
[~, index] = min(D);
r = zeros(g,n);
for j = 1:n
r(index(j),j) = 1;
end
%Optimization
Q_old = Q;
Q = 0;
for i = 1:g
for j = 1:n
Q = Q - r(i,j)*(x(:,j)-x(:,median(1,i)))'*(x(:,j)-x(:,median(1,i)));
end
end
%M-step
for i = 1:g
index = find(r(i,:)==1);
rho = zeros(1,sum(r(i,:),2));
for j = 1:sum(r(i,:),2)
rho(1,j) = sum(sum(abs(x(:,index)-x(:,index(1,j)))));
end
[~,num] = min(rho');
median(1,i) = index(1,num);
end
%Print information
disp(['itr£º',num2str(itr),'£¬Q£º',num2str(Q)]);
if isnan(Q) == 1
break;
end
if abs(Q_old-Q)<0.1
count = count + 1;
else
count = 0;
end
if count > 3
break;
end
end
%Clustering
[~,index] = min(D);
idx = index';
mu = x(:,median(1,:))';
end