文章目录
一、聚类算法原理二、Spss实现聚类三、Matlab实现聚类图片来源于清风老师视频
b站地址:数学建模学习交流
一、聚类算法原理
二、Spss实现聚类
三、Matlab实现聚类
1.创建DBSCAN.m文件内容如下:
function [IDX, isnoise]=DBSCAN(X,epsilon,MinPts)C=0;n=size(X,1);IDX=zeros(n,1); % 初始化全部为0,即全部为噪音点D=pdist2(X,X);visited=false(n,1);isnoise=false(n,1);for i=1:nif ~visited(i)visited(i)=true;Neighbors=RegionQuery(i);if numel(Neighbors)<MinPts% X(i,:) is NOISEisnoise(i)=true;elseC=C+1;ExpandCluster(i,Neighbors,C);endendendfunction ExpandCluster(i,Neighbors,C)IDX(i)=C;k = 1;while truej = Neighbors(k);if ~visited(j)visited(j)=true;Neighbors2=RegionQuery(j);if numel(Neighbors2)>=MinPtsNeighbors=[Neighbors Neighbors2]; %#okendendif IDX(j)==0IDX(j)=C;endk = k + 1;if k > numel(Neighbors)break;endendendfunction Neighbors=RegionQuery(i)Neighbors=find(D(i,:)<=epsilon);endend
2.创建PlotClusterinResult.m文件内容如下:
function PlotClusterinResult(X, IDX)k=max(IDX);Colors=hsv(k);Legends = {};for i=0:kXi=X(IDX==i,:);if i~=0Style = 'x';MarkerSize = 8;Color = Colors(i,:);Legends{end+1} = ['Cluster #' num2str(i)];elseStyle = 'o';MarkerSize = 6;Color = [0 0 0];if ~isempty(Xi)Legends{end+1} = 'Noise';endendif ~isempty(Xi)plot(Xi(:,1),Xi(:,2),Style,'MarkerSize',MarkerSize,'Color',Color);endhold on;endhold off;axis equal;grid on;legend(Legends);legend('Location', 'NorthEastOutside');end
3.创建数据文件mydata.mat内容为2列n行(下面是随机创建的数据):
4.最后,创建main.m文件内容如下:
clc;clear;close all;%% Load Dataload mydata;%% Run DBSCAN Clustering Algorithmepsilon=0.3;MinPts=10;IDX=DBSCAN(X,epsilon,MinPts);%% Plot Results% 如果只要两个指标的话就可以画图PlotClusterinResult(X, IDX);title(['DBSCAN Clustering (\epsilon = ' num2str(epsilon) ', MinPts = ' num2str(MinPts) ')']);
5.运行main.m文件,效果如下:
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