KMeans

Instance Constructors

new KMeans(data: GenSeq[Cz[ID, O, V]], k: Int, epsilon: Double, maxIterations: Int, metric: D = new Euclidean[V](squareRoot = true), initializedCenters: HashMap[Int, V] = mutable.HashMap.empty[Int, V])(implicit arg0: Numeric[ID], arg1: ClassTag[V])

data
: preferably and ArrayBuffer or ParArray of Clusterizable
k
: number of clusters
epsilon
: minimal threshold under which we consider a centroid has converged
maxIterations
: maximal number of iteration
metric
: a defined dissimilarity measure, it can be custom by overriding ContinuousDistance distance function

Type Members

type ClusterID = Int

Definition Classes
ClusteringCommons
type ID = ID

Definition Classes
DataSetsTypes

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
def areCentersMovingEnough(kCentersBeforeUpdate: HashMap[Int, V], centers: HashMap[Int, V], epsilon: Double): Boolean

Check if centers move enough
Check if centers move enough
returns
true if every centers move less than epsilon

Attributes
protected
Definition Classes
KCommons
final def asInstanceOf[T0]: T0

Definition Classes
Any
val centers: HashMap[Int, V]

Definition Classes
KCommonsScala
val centersCardinality: HashMap[Int, Int]

Definition Classes
KCommonsScala
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
def clusterizedAndSaveCenters(vectorizedDataset: GenSeq[V], centers: HashMap[Int, V]): (GenSeq[(V, Int)], HashMap[Int, V])

Attributes
protected
Definition Classes
KCommons
def clusterizedAndSaveCentersWithResetingCentersCardinalities(centers: HashMap[Int, V], centersCardinality: HashMap[Int, Int]): (GenSeq[(V, Int)], HashMap[Int, V])

Attributes
protected
Definition Classes
KCommonsScala
val dim: Int

Attributes
protected
Definition Classes
KCommonsVectors
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
lazy val kmppInitialization: (GenSeq[V], Int) ⇒ HashMap[Int, V]

Kmeans++ initialization
Kmeans++ initialization
References
- Tapas Kanungo, David M. Mount, Nathan S. Netanyahu, Christine D. Piatko, Ruth Silverman, and Angela Y. Wu. An Efficient k-Means Clustering Algorithm: Analysis and Implementation. IEEE TRANS. PAMI, 2002.
- D. Arthur and S. Vassilvitskii. "K-means++: the advantages of careful seeding". ACM-SIAM symposium on Discrete algorithms, 1027-1035, 2007.
- Anna D. Peterson, Arka P. Ghosh and Ranjan Maitra. A systematic evaluation of different methods for initializing the K-means clustering algorithm. 2010.
Definition Classes
KCommons
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
def obtainMedoid(gs: GenSeq[V]): V

Compute the similarity matrix and extract point which is the closest from all other point according to its dissimilarity measure
Compute the similarity matrix and extract point which is the closest from all other point according to its dissimilarity measure

Attributes
protected
Definition Classes
KCommons
def obtainNearestCenterID(v: V, centers: HashMap[Int, V]): ClusterID

Attributes
protected
Definition Classes
KCommons
def removeEmptyClusters(centers: HashMap[Int, V], kCentersBeforeUpdate: HashMap[Int, V], centersCardinality: HashMap[Int, Int]): Unit

Check if there are empty centers and remove them
Check if there are empty centers and remove them

Attributes
protected
Definition Classes
KCommons
def removeEmptyClustersAndCheckIfallCentersHaveConverged(centers: HashMap[Int, V], kCentersBeforeUpdate: HashMap[Int, V], centersCardinality: HashMap[Int, Int], epsilon: Double): Boolean

Attributes
protected
Definition Classes
KCommons
def resetCentersCardinality(centersCardinality: HashMap[Int, Int]): Unit

Reinitialization of cardinalities
Reinitialization of cardinalities

Attributes
protected
Definition Classes
KCommons
def run(): KMeansModel[ID, O, V, Cz[ID, O, V], D]

Run the K-Means
Run the K-Means

Definition Classes
KMeans → ClusteringAlgorithms
def runKAlgorithmWithCustomMetric(maxIterations: Int, epsilon: Double): Unit

Attributes
protected
Definition Classes
KCommonsScala
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
def updateCentersAndCardinalitiesCustom(clusterized: GenSeq[(V, Int)], centers: HashMap[Int, V], centersCardinality: HashMap[Int, Int]): Unit

Update Center and Cardinalities
Update Center and Cardinalities

Attributes
protected
Definition Classes
KCommonsScala
val vectorizedDataset: GenSeq[V]

Definition Classes
KCommonsScala
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 KMeans | package kmeans

class KMeans[ID, O, V <: Seq[Double], Cz[ID, O, V <: Seq[Double]] <: RealClusterizable[ID, O, V, Cz[ID, O, V]], D <: ContinuousDistance[V]] extends KCommonsVectors[ID, Double, V, D, Cz[ID, O, V]]

Instance Constructors

new KMeans(data: GenSeq[Cz[ID, O, V]], k: Int, epsilon: Double, maxIterations: Int, metric: D = new Euclidean[V](squareRoot = true), initializedCenters: HashMap[Int, V] = mutable.HashMap.empty[Int, V])(implicit arg0: Numeric[ID], arg1: ClassTag[V])

Type Members

type ClusterID = Int

type ID = ID

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

def areCentersMovingEnough(kCentersBeforeUpdate: HashMap[Int, V], centers: HashMap[Int, V], epsilon: Double): Boolean

final def asInstanceOf[T0]: T0

val centers: HashMap[Int, V]

val centersCardinality: HashMap[Int, Int]

def clone(): AnyRef

def clusterizedAndSaveCenters(vectorizedDataset: GenSeq[V], centers: HashMap[Int, V]): (GenSeq[(V, Int)], HashMap[Int, V])

def clusterizedAndSaveCentersWithResetingCentersCardinalities(centers: HashMap[Int, V], centersCardinality: HashMap[Int, Int]): (GenSeq[(V, Int)], HashMap[Int, V])

val dim: Int

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

lazy val kmppInitialization: (GenSeq[V], Int) ⇒ HashMap[Int, V]

References

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def obtainMedoid(gs: GenSeq[V]): V

def obtainNearestCenterID(v: V, centers: HashMap[Int, V]): ClusterID

def removeEmptyClusters(centers: HashMap[Int, V], kCentersBeforeUpdate: HashMap[Int, V], centersCardinality: HashMap[Int, Int]): Unit

def removeEmptyClustersAndCheckIfallCentersHaveConverged(centers: HashMap[Int, V], kCentersBeforeUpdate: HashMap[Int, V], centersCardinality: HashMap[Int, Int], epsilon: Double): Boolean

def resetCentersCardinality(centersCardinality: HashMap[Int, Int]): Unit

def run(): KMeansModel[ID, O, V, Cz[ID, O, V], D]

def runKAlgorithmWithCustomMetric(maxIterations: Int, epsilon: Double): Unit

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

def toString(): String

def updateCentersAndCardinalitiesCustom(clusterized: GenSeq[(V, Int)], centers: HashMap[Int, V], centersCardinality: HashMap[Int, Int]): Unit

val vectorizedDataset: GenSeq[V]

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from KCommonsVectors[ID, Double, V, D, Cz[ID, O, V]]

Inherited from KCommonsScala[ID, V, D, Cz[ID, O, V]]

Inherited from KCommons[ID, V, D]

Inherited from ClusteringAlgorithms[ID]

Inherited from Serializable

Inherited from Serializable

Inherited from DataSetsTypes[ID]

Inherited from ClusteringCommons

Inherited from AnyRef

Inherited from Any

Ungrouped