optics聚类算法matlab代码

OPTICS（Ordering Points To Identify the Clustering Structure）是一种基于密度的聚类算法，它可以在不指定聚类数的情况下发现数据集中的聚类结构。关于OPTICS聚类算法的Matlab代码如下所示：

function [reachability, order] = optics(X, minPts, epsilon)
    % X: n-by-p data matrix
    % minPts: minimum number of points required to form a cluster
    % epsilon: maximum radius of the neighborhood
    
    n = size(X, 1);
    D = pdist2(X, X);
    
    reachability = inf(1, n);
    order = zeros(1, n);
    processed = false(1, n);
    
    coreDistances = zeros(1, n);
    [sortedDistances, ~] = sort(D, 2);
    coreDistances = sortedDistances(:, minPts);
    coreDistances(coreDistances == 0) = eps;  % Avoid dividing by zero
    
    for i = 1:n
        if ~processed(i)
            processed(i) = true;
            seeds = regionQuery(i, epsilon);
            [seeds, reachability] = update(seeds, i, coreDistances, reachability);
            
            while ~isempty(seeds)
                current = seeds(1);
                seeds = seeds(2:end);
                processed(current) = true;
                seeds = regionQuery(current, epsilon);
                [seeds, reachability] = update(seeds, current, coreDistances, reachability);
            end
        end
    end
    
    [~, order] = sort(reachability, 'descend');
end

function seeds = regionQuery(p, epsilon)
    % Find all points within distance epsilon from point p
    
    global D;
    seeds = find(D(p, :) <= epsilon);
end

function [seeds, reachability] = update(seeds, p, coreDistances, reachability)
    % Update the reachability distance of the points in seeds
    
    global D;
    n = length(seeds);
    
    for i = 1:n
        q = seeds(i);
        dist = max(coreDistances(p), D(p, q));
        if dist < reachability(q)
            reachability(q) = dist;
            seeds(end+1) = q;
        end
    end
end

这段代码实现了OPTICS聚类算法的核心逻辑，并返回每个数据点的可达距离（reachability）和排序结果（order）。要注意的是，这里的代码仅供参考，可能需要根据具体的数据集和问题进行适当的修改和调整。