org.apache.spark.mllib.clustering
PowerIterationClustering
Companion object PowerIterationClustering
class PowerIterationClustering extends Serializable
Power Iteration Clustering (PIC), a scalable graph clustering algorithm developed by Lin and Cohen. From the abstract: PIC finds a very low-dimensional embedding of a dataset using truncated power iteration on a normalized pair-wise similarity matrix of the data.
- Annotations
- @Since( "1.3.0" )
- Source
- PowerIterationClustering.scala
- See also
- Alphabetic
- By Inheritance
- PowerIterationClustering
- Serializable
- Serializable
- AnyRef
- Any
- Hide All
- Show All
- Public
- All
Instance Constructors
-
new
PowerIterationClustering()
Constructs a PIC instance with default parameters: {k: 2, maxIterations: 100, initMode: "random"}.
Constructs a PIC instance with default parameters: {k: 2, maxIterations: 100, initMode: "random"}.
- Annotations
- @Since( "1.3.0" )
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[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( ... ) @native()
-
final
def
eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
def
equals(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
def
finalize(): Unit
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( classOf[java.lang.Throwable] )
-
final
def
getClass(): Class[_]
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
-
def
hashCode(): Int
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
-
final
def
isInstanceOf[T0]: Boolean
- Definition Classes
- Any
-
final
def
ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
final
def
notify(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
-
final
def
notifyAll(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
-
def
run(similarities: JavaRDD[(Long, Long, Double)]): PowerIterationClusteringModel
A Java-friendly version of
PowerIterationClustering.run
.A Java-friendly version of
PowerIterationClustering.run
.- Annotations
- @Since( "1.3.0" )
-
def
run(similarities: RDD[(Long, Long, Double)]): PowerIterationClusteringModel
Run the PIC algorithm.
Run the PIC algorithm.
- similarities
an RDD of (i, j, sij) tuples representing the affinity matrix, which is the matrix A in the PIC paper. The similarity sij must be nonnegative. This is a symmetric matrix and hence sij = sji. For any (i, j) with nonzero similarity, there should be either (i, j, sij) or (j, i, sji) in the input. Tuples with i = j are ignored, because we assume sij = 0.0.
- returns
a PowerIterationClusteringModel that contains the clustering result
- Annotations
- @Since( "1.3.0" )
-
def
run(graph: Graph[Double, Double]): PowerIterationClusteringModel
Run the PIC algorithm on Graph.
Run the PIC algorithm on Graph.
- graph
an affinity matrix represented as graph, which is the matrix A in the PIC paper. The similarity sij represented as the edge between vertices (i, j) must be nonnegative. This is a symmetric matrix and hence sij = sji. For any (i, j) with nonzero similarity, there should be either (i, j, sij) or (j, i, sji) in the input. Tuples with i = j are ignored, because we assume sij = 0.0.
- returns
a PowerIterationClusteringModel that contains the clustering result
- Annotations
- @Since( "1.5.0" )
-
def
setInitializationMode(mode: String): PowerIterationClustering.this.type
Set the initialization mode.
Set the initialization mode. This can be either "random" to use a random vector as vertex properties, or "degree" to use normalized sum similarities. Default: random.
- Annotations
- @Since( "1.3.0" )
-
def
setK(k: Int): PowerIterationClustering.this.type
Set the number of clusters.
Set the number of clusters.
- Annotations
- @Since( "1.3.0" )
-
def
setMaxIterations(maxIterations: Int): PowerIterationClustering.this.type
Set maximum number of iterations of the power iteration loop
Set maximum number of iterations of the power iteration loop
- Annotations
- @Since( "1.3.0" )
-
final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
-
def
toString(): String
- Definition Classes
- AnyRef → Any
-
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( ... ) @native()