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?