See: Description
Interface | Description |
---|---|
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 | Description |
---|---|
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 |
Output of each clustering algorithm is either a hard or soft assignment of items to clusters.
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