The BFR algorithm, named after its inventors Bradley, Fayyad and Reina, is a variant of k-means algorithm that is designed to cluster data in a high-dimensional Euclidean space.
It makes a very strong assumption about the shape of clusters: they must be normally distributed about a centroid.
The mean and standard deviation for a cluster may differ for different dimensions, but the dimensions must be independent.
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