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最新推荐文章于 2024-10-25 14:06:52 发布

nomad2

最新推荐文章于 2024-10-25 14:06:52 发布

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分类专栏： Data Mining 文章标签： matlab matrix random function each

本文链接：https://blog.youkuaiyun.com/nomad2/article/details/7185665

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1. 基于距离的迭代聚类

k-means 一个matlab实现： from: http://www.newsmth.net/nForum/article/AI/86245?p=1

function label = litekmeans(X, k)
n = size(X,2);
last = 0;
label = ceil(k*rand(1,n));  % random initialization
while any(label ~= last)
    [~,~,label] = unique(label);   % remove empty clusters
    E = sparse(1:n,label,1,n,k,n);  % transform label into indicator matrix
    center = X*(E*spdiags(1./sum(E,1)',0,k,k));    % compute center of each cluster
    last = label;
    [~,label] = max(bsxfun(@minus,center'*X,0.5*sum(center.^2,1)'),[],1); % assign samples to the nearest centers
end

2. 快速距离计算： KD树和球树