使用matlab对输入数据进行DBscan聚类。算法的思想来自基于密度方法的聚类
可直接运行代码以及数据文件可从此下载
clear;
close all;
clc;
k = 3;
Eps = 2;
%% 生成模拟数据
n = 200;
a = linspace(0,8*pi,n/2);
u = [5*cos(a)+5 10*cos(a)+5]'+1*rand(n,1);
v = [5*sin(a)+5 10*sin(a)+5]'+1*rand(n,1);
mu1 = [20 20];
S1 = [10 0;0 10];
data1 = mvnrnd(mu1,S1,100);
data = [u v;data1];
% image = imread('data.png');
% image = image(:,:,1);
% [x,y]=find(image == 0);
% data=[x,y];
%% 准备变量,输出原始结果
[m,n] = size(data);
data=[(1:m)',data];
n = n + 1;
type = zeros(1,m);
cluster_No = 1;
visited = zeros(m,1);
class = zeros(1,m)-2;
figure(2);
plot(data(:,2),data(:,3),'k.');
grid on
daspect([1 1 1]);
xlabel('x');ylabel('y');
title('原始输入点');
hold on;
%% DBscan
Kdtree = KDTreeSearcher(data(:,2:3));
for i = 1:m
% 抽取一个未访问点
if visited(i)==0
% 标为访问
visited(i) = 1;
point_now = data(i,:);
Idx_range = rangesearch(Kdtree, point_now(2:3), Eps);
index = Idx_range{
1};
if length(index) > k
class(i) = cluster_No;
while index
if visited(index(1)) == 0
visited(index(1)) = 1;
if class(index(1)) <= 0
class(index(1)) = cluster_No;
end
point_now = data(index(1),:);
Idx_range = rangesearch(Kdtree, point_now(2:3), Eps);
index_temp = Idx_range{
1};
index(1) = [];
if length(index_temp) > k
index = [index, index_temp];
end
else
index(1) = [];
end
end
cluster_No = cluster_No + 1;
end
end
end
%% DBscan聚类结果
figure;
for i = 1: cluster_No
color = [rand(),rand(),rand()];
data_class = data(find(class==i),:);
plot(data_class(:,2),data_class(:,3),'.','Color',color,'MarkerFaceColor',color);
hold on
end
data_class = data(find(class<=0),:);
plot(data_class(:,2),data_class(:,3),'k*');
hold on
grid on
daspect([1 1 1]);
xlabel('x');ylabel('y');
title('DBscan聚类结果');