BFR algorithm
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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.[1] In other words, the data must take the shape of axis-aligned ellipses.
References
[edit]- ↑ Rajaraman, Anand; Ullman, Jeffrey; Leskovec, Jure (2011). Mining of Massive Datasets. New York, NY, USA: Cambridge University Press. pp. 257–258. ISBN 978-1107015357.
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