Matlab:K-means clustering

Question

I have a matrice of A(369x10) which I want to cluster in 19 clusters. I use this method

[idx ctrs]=kmeans(A,19)

which yields idx(369x1) and ctrs(19x10)

I get the point up to here.All my rows in A is clustered in 19 clusters.

Now I have an array B(49x10).I want to know where the rows of this B corresponds in the among given 19 clusters.

How is it possible in MATLAB?

Thank you in advance

Answer 1

I can't think of a better way to do it than what you described. A built-in function would save one line, but I couldn't find one. Here's the code I would use:

[ids ctrs]=kmeans(A,19);
D = dist([testpoint;ctrs]); %testpoint is 1x10 and D will be 20x20
[distance testpointID] = min(D(1,2:end));

Answer 2

for small amount of data, you could do

[testpointID,dum] = find(permute(all(bsxfun(@eq,B,permute(ctrs,[3,2,1])),2),[3,1,2]))

but this is somewhat obscure; the bsxfun with the permuted ctrs creates a 49 x 10 x 19 array of booleans, which is then 'all-ed' across the second dimension, permuted back and then the row ids are found. again, probably not practical for large amounts of data.

Answer 3

Assuming you're using squared euclidean distance metric, try this:

for i = 1:size(ctrs,2)
d(:,i) = sum((B-ctrs(repmat(i,size(B,1),1),:)).^2,2);
end
[distances,predicted] = min(d,[],2)

predicted should then contain the index of the closest centroid, and distances should contain the distances to the closest centroid.

Take a look inside the kmeans function, at the subfunction 'distfun'. This shows you how to do the above, and also contains the equivalents for other distance metrics.

Answer 4

The following is a a complete example on clustering:

%% generate sample data
K = 3;
numObservarations = 100;
dimensions = 3;
data = rand([numObservarations dimensions]);

%% cluster
opts = statset('MaxIter', 500, 'Display', 'iter');
[clustIDX, clusters, interClustSum, Dist] = kmeans(data, K, 'options',opts, ...
    'distance','sqEuclidean', 'EmptyAction','singleton', 'replicates',3);

%% plot data+clusters
figure, hold on
scatter3(data(:,1),data(:,2),data(:,3), 50, clustIDX, 'filled')
scatter3(clusters(:,1),clusters(:,2),clusters(:,3), 200, (1:K)', 'filled')
hold off, xlabel('x'), ylabel('y'), zlabel('z')

%% plot clusters quality
figure
[silh,h] = silhouette(data, clustIDX);
avrgScore = mean(silh);


%% Assign data to clusters
% calculate distance (squared) of all instances to each cluster centroid
D = zeros(numObservarations, K);     % init distances
for k=1:K
    %d = sum((x-y).^2).^0.5
    D(:,k) = sum( ((data - repmat(clusters(k,:),numObservarations,1)).^2), 2);
end

% find  for all instances the cluster closet to it
[minDists, clusterIndices] = min(D, [], 2);

% compare it with what you expect it to be
sum(clusterIndices == clustIDX)