DBI算法matlab

DBI算法是一种评估聚类算法的度量指标，用于优化聚类算法中的k值选择。它通过计算类内距离之和与类间距离之比来判断聚类的效果，以避免K-means算法中出现局部最优解的情况。在Matlab中，可以使用以下代码来计算DBI指数：

function dbi = calculateDBI(data, labels, centroids)
    k = max(labels);
    sum_db = 0;
    for i = 1:k
        intra_cluster_dist = calculateIntraClusterDist(data(labels == i,:), centroids(i,:));
        inter_cluster_dist = calculateInterClusterDist(centroids, i);
        db = (intra_cluster_dist + inter_cluster_dist) / intra_cluster_dist;
        sum_db = sum_db + db;
    end
    dbi = sum_db / k;
end

function dist = calculateIntraClusterDist(data, centroid)
    n = size(data, 1);
    dist = sum(sum((data - repmat(centroid, n, 1)).^2, 2)) / n;
end

function dist = calculateInterClusterDist(centroids, cluster_idx)
    k = size(centroids, 1);
    dist = 0;
    for i = 1:k
        if i ~= cluster_idx
            dist = dist + euclideanDist(centroids(cluster_idx,:), centroids(i,:));
        end
    end
    dist = dist / (k - 1);
end

function dist = euclideanDist(a, b)
    dist = sqrt(sum((a - b).^2));
end