## Package org.apache.mahout.clustering

See:
Description

Interface Summary
Cluster: Implementations of this interface have a printable representation and certain attributes that are common across all clustering implementations
GaussianAccumulator
Model<O>: A model is a probability distribution over observed data points and allows the probability of any data point to be computed.
ModelDistribution<O>: A model distribution allows us to sample a model from its prior distribution.

Class Summary
AbstractCluster
ClusteringUtils
OnlineGaussianAccumulator: An online Gaussian statistics accumulator based upon Knuth (who cites Welford) which is declared to be numerically-stable.
RunningSumsGaussianAccumulator: An online Gaussian accumulator that uses a running power sums approach as reported on http://en.wikipedia.org/wiki/Standard_deviation Suffers from overflow, underflow and roundoff error but has minimal observe-time overhead
UncommonDistributions

## Package org.apache.mahout.clustering Description

This package provides several clustering algorithm implementations. Clustering usually groups a set of objects into groups of similar items. The definition of similarity usually is up to you - for text documents, cosine-distance/-similarity is recommended. Mahout also features other types of distance measure like Euclidean distance.

Input of each clustering algorithm is a set of vectors representing your items. For texts in general these are TFIDF or Bag of words representations of the documents.

Output of each clustering algorithm is either a hard or soft assignment of items to clusters.

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