HierarchicalClustering

Instance Constructors

new HierarchicalClustering(json: JsValue)
new HierarchicalClustering(k: Int = HierarchicalClustering.k, metric: String = HierarchicalClustering.metric)

k
Desired number of clusters
metric
Desired distance metric for clustering

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
final def asInstanceOf[T0]: T0

Definition Classes
Any
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
def clusterMeans(): List[List[List[Double]]]

Returns the training evolution of the cluster means
Returns the training evolution of the cluster means

Definition Classes
HierarchicalClustering → Clustering
def diagnostics(): Map[String, List[(Double, Double)]]

Provides meta-information on the algorithm
Provides meta-information on the algorithm
returns
Map object of metric names and metric values

Definition Classes
Clustering
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
val hier: Hierarchical
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
val name: String

The name of the clustering algorithm
The name of the clustering algorithm

Definition Classes
HierarchicalClustering → Clustering
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
def predict(X: List[List[Double]]): List[Int]

Applies the trained algorithm to a dataset
Applies the trained algorithm to a dataset
X
List of data instances
returns
List of predictions

Definition Classes
HierarchicalClustering → Clustering
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
def train(X: List[List[Double]]): Unit

Performs the training of the classifier
Performs the training of the classifier
X
List of training instances

Definition Classes
HierarchicalClustering → Clustering
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( ... )

Related Docs: object HierarchicalClustering | package clustering

class HierarchicalClustering extends Clustering

Instance Constructors

new HierarchicalClustering(json: JsValue)

new HierarchicalClustering(k: Int = HierarchicalClustering.k, metric: String = HierarchicalClustering.metric)

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

def clusterMeans(): List[List[List[Double]]]

def diagnostics(): Map[String, List[(Double, Double)]]

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

val hier: Hierarchical

final def isInstanceOf[T0]: Boolean

val name: String

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def predict(X: List[List[Double]]): List[Int]

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

def toString(): String

def train(X: List[List[Double]]): Unit

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from Clustering

Inherited from AnyRef

Inherited from Any

Ungrouped