Canopy clustering algorithm

The canopy clustering algorithm 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 because of the size of the data set.

The algorithm proceeds as follows:

• Cheaply partition the data into overlapping subsets, called 'canopies'
• Perform more expensive clustering, but only within these canopies. This reduces the number of instances of training data (and for K-Means the number of clusters) that must be compared at each step

External links

• Cluster Computing and MapReduce Lecture 4 from Google

See also

• Data clustering
• K-Means Algorithm
• Linde-Buzo-Gray Algorithm