Jan 7, 2014 · I said that k-means does not use pairwise distances. It is clearly stated. It uses distances to centroid. But that automatically means that it ...
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Feb 27, 2018 · Although it's generally always described with the objective you posted (which, yes, does imply euclidean distance), you can really drop in ...
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Apr 3, 2011 · Yes you can use a difference metric function; however, by definition, the k-means clustering algorithm relies on the eucldiean distance from the ...
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k-means clustering minimizes within-cluster variances (squared Euclidean distances), but not regular Euclidean distances, which would be the more difficult ...
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Nov 8, 2014 · My question: If I use Manhattan distance (L1 norm) is used instead of euclidean, is there any guarantee that the algorithm still minimizes SSE?
May 31, 2018 · K-means typically uses Euclidean distances (see e.g. https://stats.stackexchange.com/questions/81481/why-does-k-means-clustering-algorithm-use ...
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The k-means algorithm uses an iterative approach to find the optimal cluster assignments by minimizing the sum of squared distances between data points and ...
Apr 13, 2023 · K-Means clustering is an unsupervised learning algorithm. Learn to understand the types of clustering, its applications, how does it work ...
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