K-means 聚類演算法MATLAB程式碼
%----------------------main function-----------------------------
%% Clear Memory & Command Window
clcclear
close all
%% Generate Points
Sigma = [0.5 0.05; 0.05 0.5];
f1 = mvnrnd([0.5 0] ,Sigma,100);
f2 = mvnrnd([0.5 0.5],Sigma,100);
f3 = mvnrnd([0.5 1] ,Sigma,100);f4 = mvnrnd([0.5 1.5],Sigma,100);
%% K-means options
feature_vector = F; % Input
number_of_clusters = 8; % Number of Clusters
Kmeans_iteration = 40; % K-means Iteration
%% Test K-means
[cluster_centers, data] = km_fun(feature_vector, number_of_clusters, Kmeans_iteration); % K-means clusterig
%% Plot
CV = '+r+b+c+m+k+yorobocomokoysrsbscsmsksy'; % Color Vector
hold on
for i = 1 : number_of_clusters
PT = feature_vector(data(:, number_of_clusters+1) == i, :); % Find points of each cluster
plot(PT(:, 1),PT(:, 2),CV(2*i-1 : 2*i), 'LineWidth', 2); % Plot points with determined color and shape
plot(cluster_centers(:, 1), cluster_centers(:, 2), '*k', 'LineWidth', 7); % Plot cluster centers
end
hold off
grid on
%----------------------subfunction----------------------------------------
%% K-means
function [CENTS, DAL] = km_fun(F, K, KMI)
CENTS = F( ceil(rand(K,1)*size(F,1)) ,:); % Cluster Centers
DAL = zeros(size(F,1),K+2); % Distances and Labels
for n = 1:KMI
for i = 1:size(F,1)
for j = 1:K
DAL(i,j) = norm(F(i,:) - CENTS(j,:)); % compute the distances to every centers
end
[Distance, CN] = min(DAL(i,1:K)); % 1:K are Distance from Cluster Centers 1:K
DAL(i,K+1) = CN; % K+1 is Cluster Label
DAL(i,K+2) = Distance; % K+2 is Minimum Distance
end
for i = 1:K
A = (DAL(:,K+1) == i); % Cluster K Points
CENTS(i,:) = mean(F(A,:)); % New Cluster Centers
if sum(isnan(CENTS(:))) ~= 0 % If CENTS(i,:) Is Nan Then Replace It With Random Point
NC = find(isnan(CENTS(:,1)) == 1); % Find Nan Centers
for Ind = 1:size(NC,1)
CENTS(NC(Ind),:) = F(randi(size(F,1)),:);
end
end
end
end
end