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I'm new to scikit.
I can't find an example using a precomputed distance matrix in Scikit KMeans.
Could anybody shed a light now this, better with an example?
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Sklearn Kmeans uses the Euclidean distance. It has no metric parameter.
K-Means uses euclidean distance, as the default distance metric, for clustering.
K-means, as the name indicates, uses means. Computing the arithmetic mean requires access to the original features, a distance matrix cannot be used. K-means also does not use pairwise distances. So the distance matrix is useless for this algorithm.
Hey, to my knowledge, the R function hclust is able to generate clustering from a distance matrix as input such as the matrix produced by the dist function in R.
Scikit-learn does not allow you to pass in a custom (precomputed) distance matrix. It can precompute Euclidean distance matrix to speed-up the process, but there's no way to use your own one without hacking the source.
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answered Jan 02 '23 12:01
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