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See:
Description
Interface Summary | |
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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 | |
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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 |
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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