org.apache.spark.ml.clustering

Class KMeans

java.lang.Object

org.apache.spark.ml.PipelineStage

org.apache.spark.ml.Estimator<KMeansModel>

org.apache.spark.ml.clustering.KMeans

All Implemented Interfaces:

java.io.Serializable, Logging, Params, Identifiable

public class KMeans
extends Estimator<KMeansModel>

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
KMeans()
KMeans(java.lang.String uid)

Method Summary

Methods

Modifier and Type	Method and Description
KMeans	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
KMeansModel	fit(DataFrame dataset) Fits a model to the input data.
java.lang.String	getInitMode()
int	getInitSteps()
int	getK()
Param<java.lang.String>	initMode() Param for the initialization algorithm.
IntParam	initSteps() Param for the number of steps for the k-means|| initialization mode.
IntParam	k() Set the number of clusters to create (k).
static KMeans	load(java.lang.String path)
KMeans	setFeaturesCol(java.lang.String value)
KMeans	setInitMode(java.lang.String value)
KMeans	setInitSteps(int value)
KMeans	setK(int value)
KMeans	setMaxIter(int value)
KMeans	setPredictionCol(java.lang.String value)
KMeans	setSeed(long value)
KMeans	setTol(double value)
StructType	transformSchema(StructType schema) :: DeveloperApi ::
java.lang.String	uid() An immutable unique ID for the object and its derivatives.
StructType	validateAndTransformSchema(StructType schema) Validates and transforms the input schema.

Methods inherited from class org.apache.spark.ml.Estimator

fit, fit, fit, fit

Methods inherited from class org.apache.spark.ml.PipelineStage

transformSchema

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.apache.spark.ml.param.Params

clear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn, validateParams

Methods inherited from interface org.apache.spark.ml.util.Identifiable

toString

Methods inherited from interface org.apache.spark.Logging

initializeIfNecessary, initializeLogging, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning

Constructor Detail

KMeans

public KMeans(java.lang.String uid)

KMeans

public KMeans()

Method Detail

load

public static KMeans load(java.lang.String path)

uid

public java.lang.String uid()

Description copied from interface: Identifiable

An immutable unique ID for the object and its derivatives.

Specified by:

uid in interface Identifiable

Returns:

(undocumented)

copy

public KMeans copy(ParamMap extra)

Description copied from interface: Params

Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly.

Specified by:

copy in interface Params

Specified by:

copy in class Estimator<KMeansModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

See Also:

defaultCopy()

setFeaturesCol

public KMeans setFeaturesCol(java.lang.String value)

setPredictionCol

public KMeans setPredictionCol(java.lang.String value)

setK

public KMeans setK(int value)

setInitMode

public KMeans setInitMode(java.lang.String value)

setInitSteps

public KMeans setInitSteps(int value)

setMaxIter

public KMeans setMaxIter(int value)

setTol

public KMeans setTol(double value)

setSeed

public KMeans setSeed(long value)

fit

public KMeansModel fit(DataFrame dataset)

Description copied from class: Estimator

Fits a model to the input data.

Specified by:

fit in class Estimator<KMeansModel>

Parameters:

dataset - (undocumented)

Returns:

(undocumented)

transformSchema

public StructType transformSchema(StructType schema)

Description copied from class: PipelineStage

:: DeveloperApi ::

Derives the output schema from the input schema.

Specified by:

transformSchema in class PipelineStage

Parameters:

schema - (undocumented)

Returns:

(undocumented)

k

public IntParam k()

Set the number of clusters to create (k). Must be > 1. Default: 2.

Returns:

(undocumented)

getK

public int getK()

initMode

public Param<java.lang.String> initMode()

Param for the initialization algorithm. This can be either "random" to choose random points as initial cluster centers, or "k-means||" to use a parallel variant of k-means++ (Bahmani et al., Scalable K-Means++, VLDB 2012). Default: k-means||.

Returns:

(undocumented)

getInitMode

public java.lang.String getInitMode()

initSteps

public IntParam initSteps()

Param for the number of steps for the k-means|| initialization mode. This is an advanced setting -- the default of 5 is almost always enough. Must be > 0. Default: 5.

Returns:

(undocumented)

getInitSteps

public int getInitSteps()

validateAndTransformSchema

public StructType validateAndTransformSchema(StructType schema)

Validates and transforms the input schema.

Parameters:

schema - input schema

Returns:

output schema