kmeans of 3d data (clustering)

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MATTIA MERCIER
MATTIA MERCIER 2020-5-11
回答： Tara Rashnavadi 2022-11-16
I need to extend the clustering algorithm (Kmeans) to the third dimension. My dataset is composed: 700 row (different subjects) x 3 columns (each columns = different feature).
Is it possible to obtain the clustering graph in 3d? Is it possible to assign an index (for example 3 ouput, 1 2 3) for each subject based on the 3 characteristics?
For example, I want to assign to subject 1 (which corresponds three specific characteristics) a specific class belonging between one, secondo or third, (3=number of outputs defined a priori)
thanks a million

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回答（2 个）

Ameer Hamza
Ameer Hamza 2020-5-11
kmeans() supports arbitrary numbers of features, so you can definitely use 3 features. Pass these features as a column of input matrix X. For example
X = rand(1000, 3); % 1000 samples with 3 features
idx = kmeans(X, 3); % 3 number of classes
scatter3(X(:,1), X(:,2), X(:,3), 15, idx, 'filled');
Tara Rashnavadi
Tara Rashnavadi 2022-11-16

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