DBSCAN去二维离群点坐标

DBSCAN是基于密度空间的聚类算法，与KMeans算法不同，它不需要确定聚类的数量，而是基于数据推测聚类的数目，它能够针对任意形状产生聚类。

clear all;
clc;
%% 导入数据集
% data = load('testData.txt');
data = textread('F:\DATA\二维离群点检测.txt');
% 定义参数Eps和MinPts
MinPts = 5;
Eps = epsilon(data, MinPts);
[m,n] = size(data);%得到数据的大小
x = [(1:m)' data];
[m,n] = size(x);%重新计算数据集的大小
types = zeros(1,m);%用于区分核心点1，边界点0和噪音点-1
dealed = zeros(m,1);%用于判断该点是否处理过,0表示未处理过
dis = calDistance(x(:,2:n));
number = 1;%用于标记类
 
%% 对每一个点进行处理
for i = 1:m
    %找到未处理的点
    if dealed(i) == 0
        xTemp = x(i,:);
        D = dis(i,:);%取得第i个点到其他所有点的距离
        ind = find(D<=Eps);%找到半径Eps内的所有点   
        %% 区分点的类型    
        %边界点
        if length(ind) > 1 && length(ind) < MinPts+1
            types(i) = 0;
            class(i) = 0;
        end
        %噪音点
        if length(ind) == 1
            types(i) = -1;
            class(i) = -1;
            dealed(i) = 1;
        end
        %核心点(此处是关键步骤)
        if length(ind) >= MinPts+1
            types(xTemp(1,1)) = 1;
            class(ind) = number;
             
            % 判断核心点是否密度可达
            while ~isempty(ind)
                yTemp = x(ind(1),:);
                dealed(ind(1)) = 1;
                ind(1) = [];
                D = dis(yTemp(1,1),:);%找到与ind(1)之间的距离
                ind_1 = find(D<=Eps);
                 
                if length(ind_1)>1%处理非噪音点
                    class(ind_1) = number;
                    if length(ind_1) >= MinPts+1
                        types(yTemp(1,1)) = 1;
                    else
                        types(yTemp(1,1)) = 0;
                    end
                     
                    for j=1:length(ind_1)
                       if dealed(ind_1(j)) == 0
                          dealed(ind_1(j)) = 1;
                          ind=[ind ind_1(j)];  
                          class(ind_1(j))=number;
                       end                   
                   end
                end
            end
            number = number + 1;
        end
    end
end
% 最后处理所有未分类的点为噪音点
ind_2 = find(class==0);
class(ind_2) = -1;
types(ind_2) = -1;
 
%% 画出最终的聚类图
hold on
for i = 1:m
    if class(i) == -1
        plot(data(i,1),data(i,2),'.r');
    elseif class(i) == 1
        if types(i) == 1
            plot(data(i,1),data(i,2),'+b');
        else
            plot(data(i,1),data(i,2),'.b');
        end
    elseif class(i) == 2
        if types(i) == 1
            plot(data(i,1),data(i,2),'+g');
        else
            plot(data(i,1),data(i,2),'.g');
        end
    elseif class(i) == 3
        if types(i) == 1
            plot(data(i,1),data(i,2),'+c');
        else
            plot(data(i,1),data(i,2),'.c');
        end
    else
        if types(i) == 1
            plot(data(i,1),data(i,2),'+k');
        else
            plot(data(i,1),data(i,2),'.k');
        end
    end
end
hold off

 

 

 

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