What does Make Density Based Clusterer in Weka do? In Weka, there is a clustering algorithm with the name as Make Density Based Clusterer. When going through its properties, it takes a clusterer as base clusterer(I took it as K-means with k=3). 
It initially performs k-means and creates three clusters. I see prior probabilities for each cluster and attribute-wise normal distribution means and standard deviation in the result buffer.

What happens after k-means clusters are calculated?
What role mean, standard deviation and prior probabilities play here?
Why is it called density based?
Based on this paper, MakeDensityBasedClusterer is  a  metaclusterer  that  wraps  a  clustering  algorithm  to make  it  return  a  probability  distribution  and  density.   To  each  cluster  and  attribute,  it  fits  a discrete distribution or a symmetric normal distribution (whose minimum standard deviation is a parameter).