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The way k-means is constructed is not based on distances. K-means minimizes within-cluster variance. If you look at the definition of variance, it is identical
by amilios 5y ago
The way k-means is constructed is not based on distances.
K-means minimizes within-cluster variance. If you look at the definition of variance, it is identical to the sum of squared Euclidean distances from the centroid
- nerdponx 5y agoDo you lose convergence guarantees if you minimize "sum of squared distances from the centroid" using your other-than-Euclidean distance metric?