KMediansCoreSetClustering

Type Members

type CoreSet = (Array[Array[Double]], Array[Long])
case class CoreSetTree(tree: List[(Int, CoreSet)], k: Int) extends Product with Serializable
case class GridPoint(point: Array[Double]) extends Product with Serializable

Abstract Value Members

abstract def epsilon: Double

Concrete 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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object CoreSet
object CoreSetTree extends Serializable
def approxKMedian(points: Array[Array[Double]], weights: Array[Long], k: Int): (Double, Array[Array[Double]], Array[Boolean])

Returns a clustering that is within 2 times the cost of the optimal k-medians clustering.
Returns a clustering that is within 2 times the cost of the optimal k-medians clustering.
The algorithm is fairly simple. It starts with a ranomd seed cluster. It then adds a new cluster by finding the point that is farthest away from its nearest cluster. This point is the seed for a new cluster. We repeat until we have k clusters.

Note
Clustering to Minimize the Maximum Intercluster Distance, Gonzalez 1984
final def asInstanceOf[T0]: T0

Definition Classes
Any
def assign(points: Array[Array[Double]], clustering: Array[Array[Double]]): (Array[Double], Array[Int])

This returns a 2-tuple of an array of distances of each point to their nearest center and an array of cluster indexes each point belongs to.
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
def dist(x: Array[Double], y: Array[Double]): Double
def distMin(x: Array[Double], y: Array[Double]): Double
def distSq(x: Array[Double], y: Array[Double]): Double
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
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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
def kMediansCost(points: Array[Array[Double]], weights: Array[Long], centers: Array[Array[Double]], threshold: Double): Double

This returns the cost of the k-medians clustering given by centers.
This returns the cost of the k-medians clustering given by centers. The points must also be associated with a set of weights.
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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final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
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def toString(): String

Definition Classes
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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
AnyRef
Annotations
@throws( ... )
def weightArray(xs: Array[Double], ws: Array[Long]): Unit

Related Doc: package mimir

trait KMediansCoreSetClustering extends AnyRef

Type Members

type CoreSet = (Array[Array[Double]], Array[Long])

case class CoreSetTree(tree: List[(Int, CoreSet)], k: Int) extends Product with Serializable

case class GridPoint(point: Array[Double]) extends Product with Serializable

Abstract Value Members

abstract def epsilon: Double

Concrete Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

object CoreSet

object CoreSetTree extends Serializable

def approxKMedian(points: Array[Array[Double]], weights: Array[Long], k: Int): (Double, Array[Array[Double]], Array[Boolean])

final def asInstanceOf[T0]: T0

def assign(points: Array[Array[Double]], clustering: Array[Array[Double]]): (Array[Double], Array[Int])

def clone(): AnyRef

def dist(x: Array[Double], y: Array[Double]): Double

def distMin(x: Array[Double], y: Array[Double]): Double

def distSq(x: Array[Double], y: Array[Double]): Double

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

def kMediansCost(points: Array[Array[Double]], weights: Array[Long], centers: Array[Array[Double]], threshold: Double): Double

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

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

def weightArray(xs: Array[Double], ws: Array[Long]): Unit

Inherited from AnyRef

Inherited from Any

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