Package | Description |
---|---|
org.apache.mahout.clustering | |
org.apache.mahout.clustering.canopy | |
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.
|
org.apache.mahout.common.distance | |
org.apache.mahout.common.parameters |
Modifier and Type | Interface and Description |
---|---|
interface |
Cluster
Implementations of this interface have a printable representation and certain
attributes that are common across all clustering implementations
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractCluster |
Modifier and Type | Class and Description |
---|---|
class |
Canopy
Deprecated.
|
Modifier and Type | Class and Description |
---|---|
class |
SoftCluster |
Modifier and Type | Class and Description |
---|---|
class |
DistanceMeasureCluster |
Modifier and Type | Class and Description |
---|---|
class |
Kluster |
Modifier and Type | Interface and Description |
---|---|
interface |
DistanceMeasure
This interface is used for objects which can determine a distance metric between two points
|
Modifier and Type | Class and Description |
---|---|
class |
ChebyshevDistanceMeasure
This class implements a "Chebyshev distance" metric by finding the maximum difference
between each coordinate.
|
class |
CosineDistanceMeasure
This class implements a cosine distance metric by dividing the dot product of two vectors by the product of their
lengths.
|
class |
EuclideanDistanceMeasure
This class implements a Euclidean distance metric by summing the square root of the squared differences
between each coordinate.
|
class |
MahalanobisDistanceMeasure |
class |
ManhattanDistanceMeasure
This class implements a "manhattan distance" metric by summing the absolute values of the difference
between each coordinate
|
class |
MinkowskiDistanceMeasure
Implement Minkowski distance, a real-valued generalization of the
integral L(n) distances: Manhattan = L1, Euclidean = L2.
|
class |
SquaredEuclideanDistanceMeasure
Like
EuclideanDistanceMeasure but it does not take the square root. |
class |
TanimotoDistanceMeasure
Tanimoto coefficient implementation.
|
class |
WeightedDistanceMeasure
Abstract implementation of DistanceMeasure with support for weights.
|
class |
WeightedEuclideanDistanceMeasure
This class implements a Euclidean distance metric by summing the square root of the squared differences
between each coordinate, optionally adding weights.
|
class |
WeightedManhattanDistanceMeasure
This class implements a "Manhattan distance" metric by summing the absolute values of the difference
between each coordinate, optionally with weights.
|
Modifier and Type | Interface and Description |
---|---|
interface |
Parameter<T>
An accessor to a parameters in the job.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractParameter<T> |
class |
ClassParameter |
class |
DoubleParameter |
class |
PathParameter |
Modifier and Type | Method and Description |
---|---|
static String |
Parametered.ParameteredGeneralizations.conf(Parametered parametered) |
static void |
Parametered.ParameteredGeneralizations.configureParameters(Parametered parametered,
org.apache.hadoop.conf.Configuration jobConf) |
static void |
Parametered.ParameteredGeneralizations.configureParameters(String prefix,
Parametered parametered,
org.apache.hadoop.conf.Configuration jobConf)
Calls
createParameters(String,org.apache.hadoop.conf.Configuration)
on parameter parmetered, and then recur down its composite tree to invoke
createParameters(String,org.apache.hadoop.conf.Configuration)
and configure(org.apache.hadoop.conf.Configuration) on
each composite part. |
static String |
Parametered.ParameteredGeneralizations.help(Parametered parametered) |
Copyright © 2008–2017 The Apache Software Foundation. All rights reserved.