基于马氏距离的DBSCAN聚类算法的matlab代码

以下是基于马氏距离的DBSCAN聚类算法的Matlab代码示例：

function [labels, numClusters] = DBSCAN_Mahalanobis(data, epsilon, minPts)
    numPoints = size(data, 1);
    labels = zeros(numPoints, 1);
    numClusters = 0;
    
    for i = 1:numPoints
        if labels(i) == 0
            neighbors = regionQuery(data, i, epsilon);
            if numel(neighbors) < minPts
                labels(i) = -1; % Noise point
            else
                numClusters = numClusters + 1;
                expandCluster(data, labels, i, neighbors, numClusters, epsilon, minPts);
            end
        end
    end
end

function expandCluster(data, labels, pointIdx, neighbors, clusterIdx, epsilon, minPts)
    labels(pointIdx) = clusterIdx;
    k = 1;
    
    while k <= numel(neighbors)
        nextIdx = neighbors(k);
        if labels(nextIdx) == -1 % Noise point previously labeled as noise
            labels(nextIdx) = clusterIdx;
        elseif labels(nextIdx) == 0 % Unclassified point
            labels(nextIdx) = clusterIdx;
            nextNeighbors = regionQuery(data, nextIdx, epsilon);
            if numel(nextNeighbors) >= minPts
                neighbors = [neighbors; nextNeighbors];
            end
        end
        
        k = k + 1;
    end
end

function neighbors = regionQuery(data, pointIdx, epsilon)
    distanceMatrix = pdist2(data, data(pointIdx,:), 'mahalanobis');
    neighbors = find(distanceMatrix <= epsilon);
end

此代码实现了基于马氏距离的DBSCAN聚类算法。`DBSCAN_Mahalanobis` 函数是主函数，接受数据集 `data`、邻域半径 `epsilon` 和最小邻域点数 `minPts` 作为输入，并返回每个数据点的簇标签和聚类数量。`expandCluster` 函数用于扩展聚类，`regionQuery` 函数用于找到给定点的邻域点。

请注意，此代码仅为示例，可能需要根据具体问题进行适当修改和调整。