WideKMeans

A class to implement a Wide K-Means.

Specify the k and iterations then access the fields like this:

val wKM = WideKMeans(5, 10)
val clusterCenters = wKM.run(data)
val resultingClusters = wKM.assignClusters(data, clusterCenters)

Linear Supertypes

AnyRef, Any

Instance Constructors

new WideKMeans(k: Int, iterations: Int)

k
the number of desired clusters
iterations
the number of iterations requested

Value Members

final def !=(arg0: Any): Boolean

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

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

Definition Classes
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final def asInstanceOf[T0]: T0

Definition Classes
Any
def assignClusters(data: RDD[Vector], clusterCenters: RDD[Vector]): Array[Int]

Assigns points to specific clusters using vectors found from clusterCentres
Assigns points to specific clusters using vectors found from clusterCentres
data
original data input
clusterCenters
result of the run function
returns
returns the cluster assignments
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
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def finalize(): Unit

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

Definition Classes
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def hashCode(): Int

Definition Classes
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final def isInstanceOf[T0]: Boolean

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

Definition Classes
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final def notify(): Unit

Definition Classes
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final def notifyAll(): Unit

Definition Classes
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def run(data: RDD[Vector]): RDD[Vector]

Specify the input data
Specify the input data
1. Splits the vectors into k dense vectors 2. Finds the Euclidean distance between the new center and the values on the graph 3. Assigns values with lowest distances to the clusters 4. Creates new dense vectors with the values found 5. Repeats till the number of iterations is met
data
Input an RDD[Vector]
returns
Returns the cluster centers as a RDD[Vector]
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
final def wait(): Unit

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

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

Definition Classes
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Annotations
@throws( ... )

Related Docs: object WideKMeans | package algo

class WideKMeans extends AnyRef

Instance Constructors

new WideKMeans(k: Int, iterations: Int)

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def assignClusters(data: RDD[Vector], clusterCenters: RDD[Vector]): Array[Int]

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

def run(data: RDD[Vector]): RDD[Vector]

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

def toString(): String

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from AnyRef

Inherited from Any

Ungrouped