Canopy clustering algorithm
The canopy clustering algorithm is an unsupervised pre-clustering algorithm introduced by Andrew McCallum, Kamal Nigam and Lyle Ungar in 2000. It is often used as preprocessing step for the K-means algorithm or the Hierarchical clustering 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.
- Comment
- enThe canopy clustering algorithm is an unsupervised pre-clustering algorithm introduced by Andrew McCallum, Kamal Nigam and Lyle Ungar in 2000. It is often used as preprocessing step for the K-means algorithm or the Hierarchical clustering 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.
- Has abstract
- enThe canopy clustering algorithm is an unsupervised pre-clustering algorithm introduced by Andrew McCallum, Kamal Nigam and Lyle Ungar in 2000. It is often used as preprocessing step for the K-means algorithm or the Hierarchical clustering 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.
- Is primary topic of
- Canopy clustering algorithm
- Label
- enCanopy clustering algorithm
- Link from a Wikipage to another Wikipage
- Andrew McCallum
- Category:Cluster analysis algorithms
- Computer cluster
- Curse of dimensionality
- Data clustering
- Data set
- Hierarchical clustering
- K-means algorithm
- SameAs
- 4ex49
- Canopy clustering algorithm
- m.03d6fbv
- Q5033439
- Subject
- Category:Cluster analysis algorithms
- WasDerivedFrom
- Canopy clustering algorithm?oldid=1108257834&ns=0
- WikiPageLength
- 2855
- Wikipage page ID
- 14526742
- Wikipage revision ID
- 1108257834
- WikiPageUsesTemplate
- Template:Reflist