BFR algorithm: Difference between revisions
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The '''BFR algorithm''', named after its inventors Bradley, Fayyad and Reina, is a variant of [[k-means clustering|k-means]] 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 [[Normal distribution|normally distributed]] about a centroid. The mean and standard deviation for a cluster may differ for different dimensions, but the dimensions must be independent. |
The '''BFR algorithm''', named after its inventors Bradley, Fayyad and Reina, is a variant of [[k-means clustering|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 [[Normal distribution|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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Revision as of 16:43, 18 May 2018
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.