Package | Description |
---|---|
org.apache.mahout.clustering | |
org.apache.mahout.clustering.canopy | |
org.apache.mahout.clustering.classify | |
org.apache.mahout.clustering.fuzzykmeans | |
org.apache.mahout.clustering.iterator | |
org.apache.mahout.clustering.kmeans |
This package provides an implementation of the k-means clustering
algorithm.
|
Class and Description |
---|
Cluster
Implementations of this interface have a printable representation and certain
attributes that are common across all clustering implementations
|
GaussianAccumulator |
Model
A model is a probability distribution over observed data points and allows
the probability of any data point to be computed.
|
Class and Description |
---|
AbstractCluster |
Cluster
Implementations of this interface have a printable representation and certain
attributes that are common across all clustering implementations
|
Model
A model is a probability distribution over observed data points and allows
the probability of any data point to be computed.
|
Class and Description |
---|
Cluster
Implementations of this interface have a printable representation and certain
attributes that are common across all clustering implementations
|
Class and Description |
---|
AbstractCluster |
Cluster
Implementations of this interface have a printable representation and certain
attributes that are common across all clustering implementations
|
Model
A model is a probability distribution over observed data points and allows
the probability of any data point to be computed.
|
Class and Description |
---|
AbstractCluster |
Cluster
Implementations of this interface have a printable representation and certain
attributes that are common across all clustering implementations
|
Model
A model is a probability distribution over observed data points and allows
the probability of any data point to be computed.
|
Class and Description |
---|
AbstractCluster |
Cluster
Implementations of this interface have a printable representation and certain
attributes that are common across all clustering implementations
|
Model
A model is a probability distribution over observed data points and allows
the probability of any data point to be computed.
|
Copyright © 2008–2017 The Apache Software Foundation. All rights reserved.