Jump to content

Canopy clustering algorithm

Add links
From Wikipedia, the free encyclopedia
This is an old revision of this page, as edited by Miniapolis (talk | contribs) at 16:04, 2 April 2011 (Wikification, removed tag). The present address (URL) is a permanent link to this revision, which may differ significantly from the current revision.

The canopy clustering algorithm, in computing, is an unsupervised clustering algorithm related to the K-means algorithm.

It is intended to speed up clustering operations on large data sets, where using another algorithm directly may be impractical due to the size of the data set.

The algorithm proceeds as follows:

  • Cheaply partitioning the data into overlapping subsets (called "canopies")
  • Perform more expensive clustering, but only within these canopies

Benefits

  • The number of instances of training data that must be compared at each step is reduced
  • There is some evidence that the resulting clusters are improved[1]

References

  1. ^ Mahout description of Canopy-Clustering Retrieved 2011-04-02.


Stub icon

This algorithms or data structures-related article is a stub. You can help Wikipedia by expanding it.

Retrieved from "https://en.wikipedia.org/w/index.php?title=Canopy_clustering_algorithm&oldid=421994721"