% Copyright (c) 2015, Yarpiz (www.yarpiz.com)
% All rights reserved. Please read the "license.txt" for license terms.
%
% Project Code: YPML110
% Project Title: Implementation of DBSCAN Clustering in MATLAB
% Publisher: Yarpiz (www.yarpiz.com)
%
% Developer: S. Mostapha Kalami Heris (Member of Yarpiz Team)
%
% Contact Info: sm.kalami@gmail.com, info@yarpiz.com
clc;
clear;
close all;
%% test Data
%数据下载网站：http://archive.ics.uci.edu/ml/machine-learning-databases/iris/
%这里使用的iris数据的一部分，由于第3维和第4为数据数据区分度好，因此用3、4维数据测试
X1 =[5.1,3.5,1.4,0.2;%,Iris-setosa
4.9,3.0,1.4,0.2;
4.7,3.2,1.3,0.2;
4.6,3.1,1.5,0.2;
5.1,3.7,1.5,0.4;
4.6,3.6,1.0,0.2;
5.1,3.3,1.7,0.5;
5.0,3.6,1.4,0.2;
5.4,3.9,1.7,0.4;
4.6,3.4,1.4,0.3;
5.0,3.4,1.5,0.2;
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;
4.8,3.0,1.4,0.1;
4.3,3.0,1.1,0.1;
5.8,4.0,1.2,0.2;
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;
6.4,3.2,4.5,1.5;%Iris-versicolor
6.9,3.1,4.9,1.5;
5.5,2.3,4.0,1.3;
6.5,2.8,4.6,1.5;
5.7,2.8,4.5,1.3;
6.3,3.3,4.7,1.6;
4.9,2.4,3.3,1.0;
4.9,2.4,3.3,1.0;
6.6,2.9,4.6,1.3;
5.2,2.7,3.9,1.4;
5.0,2.0,3.5,1.0;
5.9,3.0,4.2,1.5;
6.0,2.2,4.0,1.0];
X=X1(:,3:4);
%%KNN k distance graph, to determine the epsilon
A=X;
Kvalue=4;
numData=size(A,1);
Kdist=zeros(numData,Kvalue);
[IDX0,Dist]=knnsearch(A(2:numData,:),A(1,:),'K',Kvalue);
Kdist(1,:)=Dist;
for i=2:size(A,1)
[IDX0,Dist] = knnsearch(A([1:i-1,i+1:numData],:),A(i,:),'K',Kvalue);
Kdist(i,:)=Dist;
end
Kdist1=reshape(Kdist,size(Kdist,1)*size(Kdist,2),1);
[sortKdist,sortKdistIdx]=sort(Kdist1,'descend');
distX=[1:numData*Kvalue]';
plot(distX,sortKdist,'r+-','LineWidth',2);
set(gcf,'position',[1000 340 350 350]);
grid on;
%% Run DBSCAN Clustering Algorithm
epsilon= 0.15 ;
MinPts= 3 ;
IDX1=DBSCAN(X,epsilon,MinPts);
%% Plot Results
figure;
PlotClusterinResult(X, IDX1);
title(['DBSCAN Clustering (\epsilon = ' num2str(epsilon) ', MinPts = ' num2str(MinPts) ')']);
set(gcf,'position',[30 -10 500 500]);
epsilon= 0.25 ;
MinPts= 3 ;
IDX2=DBSCAN(X,epsilon,MinPts);
%% Plot Results
figure;
PlotClusterinResult(X, IDX2);
title(['DBSCAN Clustering (\epsilon = ' num2str(epsilon) ', MinPts = ' num2str(MinPts) ')']);
set(gcf,'position',[530 -10 500 500]);
epsilon= 0.5 ;
MinPts= 3 ;
IDX3=DBSCAN(X,epsilon,MinPts);
%% Plot Results
figure;
PlotClusterinResult(X, IDX3);
title(['DBSCAN Clustering (\epsilon = ' num2str(epsilon) ', MinPts = ' num2str(MinPts) ')']);
set(gcf,'position',[30 380 500 500]);
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