  The original k-means clustering method works only if the exact vectors
representing the data points are known. Therefore calculating the distances
from the centroids needs vector operations, since the average of abstract data
points is undefined. Existing algorithms can be extended for those cases when
the sole input is the distance matrix, and the exact representing vectors are
unknown. This extension may be named relational k-means after a notation for a
similar algorithm invented for fuzzy clustering. A method is then proposed for
generalizing k-means for scenarios when the data points have absolutely no
connection with a Euclidean space.
