dbscan聚类算法matlab代码

以下是DBSCAN聚类算法的MATLAB代码示例：

function [IDX, isnoise]=DBSCAN(X,epsilon,MinPts)
%Cite: www.cs.uic.edu/~db/dbSCAN/DBSCAN_optics.m
%Cite: www.mathworks.com/matlabcentral/fileexchange/52905-dbscan-clustering-algorithm
%Inputs:
%   X - a matrix of data points (one per row)
%   eps - neighborhood radius
%   MinPts - minimum number of points required to form a cluster
%Outputs:
%   IDX - cluster membership (0 indicates noise)
%   isnoise - logical vector indicating noise points
    C=0;
    n=size(X,1);
    D=pdist2(X,X); % pairwise distance matrix
    visited=false(n,1);
    IDX=zeros(n,1);
    isnoise=false(n,1);
    for i=1:n
        if ~visited(i)
            visited(i)=true;
            Neighbors=RegionQuery(i);
            if numel(Neighbors)<MinPts
                % X(i,:) is NOISE
                isnoise(i)=true;
            else
                C=C+1;
                ExpandCluster(i,Neighbors,C);
            end
        end
    end
    function ExpandCluster(i,Neighbors,C)
        IDX(i)=C;
        k = 1;
        while true
            j = Neighbors(k);
            if ~visited(j)
                visited(j)=true;
                Neighbors2=RegionQuery(j);
                if numel(Neighbors2)>=MinPts
                    Neighbors=[Neighbors Neighbors2];   %#ok
                end
            end
            if IDX(j)==0
                IDX(j)=C;
            end
            k = k + 1;
            if k > numel(Neighbors)
                break;
            end
        end
    end
    function Neighbors=RegionQuery(i)
        Neighbors=find(D(i,:)<=epsilon);
    end
end

使用方法：

给定一个n×d的数据矩阵X，其中n是数据点的数量，d是每个数据点的维数，epsilon和MinPts是DBSCAN算法的参数。

IDX = DBSCAN(X,epsilon,MinPts);

返回结果IDX是每个数据点的簇分配，0表示噪声点，isnoise是一个逻辑向量，表示哪些点是噪声点。