Why does the BFR (Bradley, Fayyad and Reina) algorithm assume clusters to be normally distributed around its centroid?
Roughly, the algorithm needs to estimate the probability to assign a point the correct cluster.
So the algorithm add P to a cluster if it is very unlikely that, after all the points have been processed, some other cluster centroid will be found to be nearer to P.
So the algorithm measure the probability that, if P belongs to a cluster, it would be found as far as it is from the centroid of that cluster. To do that, it assumes that the clusters contain normally distributed points aligned with the axes of the used space.