KMeans

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
val K_MEANS_PARALLEL: String

Annotations
@Since( "0.8.0" )
val RANDOM: String

Annotations
@Since( "0.8.0" )
final def asInstanceOf[T0]: T0

Definition Classes
Any
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def hashCode(): Int

Definition Classes
AnyRef → Any
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
def train(data: RDD[Vector], k: Int, maxIterations: Int): KMeansModel

Trains a k-means model using specified parameters and the default values for unspecified.
Trains a k-means model using specified parameters and the default values for unspecified.

Annotations
@Since( "0.8.0" )
def train(data: RDD[Vector], k: Int, maxIterations: Int, initializationMode: String): KMeansModel

Trains a k-means model using the given set of parameters.
Trains a k-means model using the given set of parameters.
data
Training points as an RDD of Vector types.
k
Number of clusters to create.
maxIterations
Maximum number of iterations allowed.
initializationMode
The initialization algorithm. This can either be "random" or "k-means||". (default: "k-means||")

Annotations
@Since( "2.1.0" )
def train(data: RDD[Vector], k: Int, maxIterations: Int, initializationMode: String, seed: Long): KMeansModel

Trains a k-means model using the given set of parameters.
Trains a k-means model using the given set of parameters.
data
Training points as an RDD of Vector types.
k
Number of clusters to create.
maxIterations
Maximum number of iterations allowed.
initializationMode
The initialization algorithm. This can either be "random" or "k-means||". (default: "k-means||")
seed
Random seed for cluster initialization. Default is to generate seed based on system time.

Annotations
@Since( "2.1.0" )
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )

Deprecated Value Members

def train(data: RDD[Vector], k: Int, maxIterations: Int, runs: Int): KMeansModel

Trains a k-means model using specified parameters and the default values for unspecified.
Trains a k-means model using specified parameters and the default values for unspecified.

Annotations
@Since( "0.8.0" ) @deprecated
Deprecated
(Since version 2.1.0) Use train method without 'runs'
def train(data: RDD[Vector], k: Int, maxIterations: Int, runs: Int, initializationMode: String): KMeansModel

Trains a k-means model using the given set of parameters.
Trains a k-means model using the given set of parameters.
data
Training points as an RDD of Vector types.
k
Number of clusters to create.
maxIterations
Maximum number of iterations allowed.
runs
This param has no effect since Spark 2.0.0.
initializationMode
The initialization algorithm. This can either be "random" or "k-means||". (default: "k-means||")

Annotations
@Since( "0.8.0" ) @deprecated
Deprecated
(Since version 2.1.0) Use train method without 'runs'
def train(data: RDD[Vector], k: Int, maxIterations: Int, runs: Int, initializationMode: String, seed: Long): KMeansModel

Trains a k-means model using the given set of parameters.
Trains a k-means model using the given set of parameters.
data
Training points as an RDD of Vector types.
k
Number of clusters to create.
maxIterations
Maximum number of iterations allowed.
runs
This param has no effect since Spark 2.0.0.
initializationMode
The initialization algorithm. This can either be "random" or "k-means||". (default: "k-means||")
seed
Random seed for cluster initialization. Default is to generate seed based on system time.

Annotations
@Since( "1.3.0" ) @deprecated
Deprecated
(Since version 2.1.0) Use train method without 'runs'

Related Docs: class KMeans | package clustering

object KMeans extends Serializable

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

val K_MEANS_PARALLEL: String

val RANDOM: String

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

final def synchronized[T0](arg0: ⇒ T0): T0

def toString(): String

def train(data: RDD[Vector], k: Int, maxIterations: Int): KMeansModel

def train(data: RDD[Vector], k: Int, maxIterations: Int, initializationMode: String): KMeansModel

def train(data: RDD[Vector], k: Int, maxIterations: Int, initializationMode: String, seed: Long): KMeansModel

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit

Deprecated Value Members

def train(data: RDD[Vector], k: Int, maxIterations: Int, runs: Int): KMeansModel

def train(data: RDD[Vector], k: Int, maxIterations: Int, runs: Int, initializationMode: String): KMeansModel

def train(data: RDD[Vector], k: Int, maxIterations: Int, runs: Int, initializationMode: String, seed: Long): KMeansModel

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped