wehub-resource-sync
29 lines
936 B
Matlab
Executable File
29 lines
936 B
Matlab
Executable File
function [label, index, energy] = kmedoids(X, init)
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% Perform k-medoids clustering.
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% Input:
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% X: d x n data matrix
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% init: k number of clusters or label (1 x n vector)
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% Output:
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% label: 1 x n sample labels
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% index: index of medoids
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% energy: optimization target value
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% Written by Mo Chen (sth4nth@gmail.com).
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[d,n] = size(X);
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if numel(init)==1
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k = init;
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label = ceil(k*rand(1,n));
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elseif numel(init)==n
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label = init;
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end
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X = X-mean(X,2); % reduce chance of numerical problems
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v = dot(X,X,1);
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D = v+v'-2*(X'*X); % Euclidean distance matrix
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D(sub2ind([d,d],1:d,1:d)) = 0; % reduce chance of numerical problems
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last = zeros(1,n);
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while any(label ~= last)
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[~,~,last(:)] = unique(label); % remove empty clusters
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[~, index] = min(D*sparse(1:n,last,1),[],1); % find k medoids
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[val, label] = min(D(index,:),[],1); % assign labels
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end
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energy = sum(val);
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