5 ms·
Not sure why this was down voted. K-means in its standard form does make a Euclidean hypothesis (it does not minimize a distance but the in-cluster variance).
by whiplash451 5y ago
Not sure why this was down voted.
K-means in its standard form does make a Euclidean hypothesis (it does not minimize a distance but the in-cluster variance).
It is not correct to use arbitrary distances because k-means may stop converging with other distance functions.
See this thread for more info: https://stats.stackexchange.com/questions/81481/why-does-k-means-clustering-algorithm-use-only-euclidean-distance-metric https://stats.stackexchange.com/questions/81481/why-does-k-m...
- isoprophlex 5y agoThat's a really pedantic take, nothing precludes you from using whatever distance function you want to get the distance to k centroids and act accordingly
- amilios 5y agoLosing convergence guarantees is somewhat important!
- isoprophlex 5y agoI totally agree with your point, but sometimes you can get away by being handwavey about it and making sure the centroids aren't skipping all over the place... and that's good enough