## Uses of Interface
org.apache.mahout.clustering.Model

Packages that use Model
org.apache.mahout.clustering: This package provides several clustering algorithm implementations.
org.apache.mahout.clustering.canopy
org.apache.mahout.clustering.dirichlet
org.apache.mahout.clustering.dirichlet.models
org.apache.mahout.clustering.fuzzykmeans
org.apache.mahout.clustering.kmeans: This package provides an implementation of the k-means clustering algorithm.
org.apache.mahout.clustering.meanshift

Uses of Model in org.apache.mahout.clustering

Subinterfaces of Model in org.apache.mahout.clustering
`interface` `Cluster` Implementations of this interface have a printable representation and certain attributes that are common across all clustering implementations

Classes in org.apache.mahout.clustering that implement Model
`class` `AbstractCluster`
`class` `DistanceMeasureCluster`

Methods in org.apache.mahout.clustering that return Model
`Model<?>` `JsonModelAdapter.deserialize(com.google.gson.JsonElement json, java.lang.reflect.Type typeOfT, com.google.gson.JsonDeserializationContext context)`
`Model<VectorWritable>` `Model.sampleFromPosterior()`
`Model<VectorWritable>` `DistanceMeasureCluster.sampleFromPosterior()`
`Model<O>[]` `ModelDistribution.sampleFromPosterior(Model<O>[] posterior)` Return a list of models sampled from the posterior
`Model<O>[]` `ModelDistribution.sampleFromPrior(int howMany)` Return a list of models sampled from the prior

Methods in org.apache.mahout.clustering with parameters of type Model
`Model<O>[]` `ModelDistribution.sampleFromPosterior(Model<O>[] posterior)` Return a list of models sampled from the posterior
`com.google.gson.JsonElement` `JsonModelAdapter.serialize(Model<?> src, java.lang.reflect.Type typeOfSrc, com.google.gson.JsonSerializationContext context)`

Constructor parameters in org.apache.mahout.clustering with type arguments of type Model
`VectorModelClassifier(java.util.List<Model<VectorWritable>> models)`

Uses of Model in org.apache.mahout.clustering.canopy

Classes in org.apache.mahout.clustering.canopy that implement Model
`class` `Canopy` This class models a canopy as a center point, the number of points that are contained within it according to the application of some distance metric, and a point total which is the sum of all the points and is used to compute the centroid when needed.

Uses of Model in org.apache.mahout.clustering.dirichlet

Classes in org.apache.mahout.clustering.dirichlet that implement Model
`class` `DirichletCluster`

Methods in org.apache.mahout.clustering.dirichlet that return Model
`Model<VectorWritable>[]` `DirichletState.getModels()`
`Model<VectorWritable>` `DirichletCluster.sampleFromPosterior()`
`protected Model<VectorWritable>[]` `DirichletClusterer.samplePosteriorModels()`

Methods in org.apache.mahout.clustering.dirichlet with parameters of type Model
`protected void` `DirichletClusterer.observe(Model<VectorWritable>[] newModels, VectorWritable observation)`
`static void` `DirichletCluster.writeModel(java.io.DataOutput out, Model<?> model)` Writes a typed Model instance to the output stream

Uses of Model in org.apache.mahout.clustering.dirichlet.models

Classes in org.apache.mahout.clustering.dirichlet.models that implement Model
`class` `AsymmetricSampledNormalModel`
`class` `GaussianCluster`
`class` `L1Model`
`class` `NormalModel`
`class` `SampledNormalModel`

Methods in org.apache.mahout.clustering.dirichlet.models that return Model
`Model<VectorWritable>` `GaussianCluster.sampleFromPosterior()`
`Model<VectorWritable>[]` `SampledNormalDistribution.sampleFromPosterior(Model<VectorWritable>[] posterior)`
`Model<VectorWritable>[]` `NormalModelDistribution.sampleFromPosterior(Model<VectorWritable>[] posterior)`
`Model<VectorWritable>[]` `L1ModelDistribution.sampleFromPosterior(Model<VectorWritable>[] posterior)`
`Model<VectorWritable>[]` `GaussianClusterDistribution.sampleFromPosterior(Model<VectorWritable>[] posterior)`
`Model<VectorWritable>[]` `DistanceMeasureClusterDistribution.sampleFromPosterior(Model<VectorWritable>[] posterior)`
`Model<VectorWritable>[]` `AsymmetricSampledNormalDistribution.sampleFromPosterior(Model<VectorWritable>[] posterior)`
`Model<VectorWritable>[]` `SampledNormalDistribution.sampleFromPrior(int howMany)`
`Model<VectorWritable>[]` `NormalModelDistribution.sampleFromPrior(int howMany)`
`Model<VectorWritable>[]` `L1ModelDistribution.sampleFromPrior(int howMany)`
`Model<VectorWritable>[]` `GaussianClusterDistribution.sampleFromPrior(int howMany)`
`Model<VectorWritable>[]` `DistanceMeasureClusterDistribution.sampleFromPrior(int howMany)`
`Model<VectorWritable>[]` `AsymmetricSampledNormalDistribution.sampleFromPrior(int howMany)`

Methods in org.apache.mahout.clustering.dirichlet.models with parameters of type Model
`Model<VectorWritable>[]` `SampledNormalDistribution.sampleFromPosterior(Model<VectorWritable>[] posterior)`
`Model<VectorWritable>[]` `NormalModelDistribution.sampleFromPosterior(Model<VectorWritable>[] posterior)`
`Model<VectorWritable>[]` `L1ModelDistribution.sampleFromPosterior(Model<VectorWritable>[] posterior)`
`Model<VectorWritable>[]` `GaussianClusterDistribution.sampleFromPosterior(Model<VectorWritable>[] posterior)`
`Model<VectorWritable>[]` `DistanceMeasureClusterDistribution.sampleFromPosterior(Model<VectorWritable>[] posterior)`
`Model<VectorWritable>[]` `AsymmetricSampledNormalDistribution.sampleFromPosterior(Model<VectorWritable>[] posterior)`

Uses of Model in org.apache.mahout.clustering.fuzzykmeans

Classes in org.apache.mahout.clustering.fuzzykmeans that implement Model
`class` `SoftCluster`

Uses of Model in org.apache.mahout.clustering.kmeans

Classes in org.apache.mahout.clustering.kmeans that implement Model
`class` `Cluster`

Uses of Model in org.apache.mahout.clustering.meanshift

Classes in org.apache.mahout.clustering.meanshift that implement Model
`class` `MeanShiftCanopy` This class models a canopy as a center point, the number of points that are contained within it according to the application of some distance metric, and a point total which is the sum of all the points and is used to compute the centroid when needed.

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