Timeline for k-means implementation with custom distance matrix in input
Current License: CC BY-SA 3.0
4 events
when toggle format | what | by | license | comment | |
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Aug 7, 2018 at 13:11 | comment | added | CpILL | There is also pyclustering a python/C++ library that lets you supply a custom metric function: github.com/annoviko/pyclustering/issues/417 | |
Apr 27, 2013 at 19:10 | comment | added | Has QUIT--Anony-Mousse | ELKI allows you to use arbitrary distance functions with k-means. Note that the algorithm may then fail to converge. K-means is really designed for squared euclidean distance (sum of squares). With other distances, the mean may no longer optimize, and boom, the algorithm will eventually not converge. Seriously, consider using k-medoids. It actually was written to allow using the k-means idea with arbirary distances. | |
Jun 30, 2011 at 19:18 | comment | added | Eugenio | Hi, thanks for the answer; instead of directly give the distance matrix would it be possible to give as input a custom distance metric? The point is that I have to compare two clusterings methods and, since in the second one I use a custom similarity matrix, I want to use the same approach with kmeans in order to get a fair comparison. | |
Jun 30, 2011 at 4:50 | history | answered | N F | CC BY-SA 3.0 |