BFR algorithm: Difference between revisions
Content deleted Content added
Add Reflist, added uncategorised tag using AWB |
added Category:Cluster analysis algorithms; removed {{uncategorized}} using HotCat |
||
| Line 5: | Line 5: | ||
{{Reflist}} |
{{Reflist}} |
||
{{Uncategorized|date=May 2018}} |
|||
[[Category:Cluster analysis algorithms]] |
|||
Revision as of 19:13, 20 May 2018
 | This article needs additional citations for verification. (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.[1]
References
- ^ Rajaraman, Anand; Ullman, Jeffrey; Leskovec, Jure (2011). Mining of Massive Datasets. New York, NY, USA: Cambridge University Press. pp. 257–258. ISBN 1107015359.