ClusteringBinaryAnalysis

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

lazy val closestClusterFromFeaturesDistribution: ClusterID

val clusteringNumber: ClusteringNumber

The number of clustering which was applied

val clustersNumbers: Int

lazy val distantFeaturesFromStdByCluster: HashMap[ClusterID, Array[Int]]

final def eq(arg0: AnyRef): Boolean

lazy val farestClusterFromFeaturesDistribution: ClusterID

val featuresNumbers: Int

val frequencyPerFeature: Array[Double]

Frequencies for each features

val frequencyPerFeatureByClusterID: HashMap[types.ClusterIDType.ClusterID, Array[Double]]

Frequencies for each features by ClusterID

final def getClass(): Class[_]

def getFeaturesFarFromStd(stdGap: Double = 1D): HashMap[ClusterID, Array[Int]]

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val occurencesPerFeature: Array[Int]

Number of occurences for each features

val occurencesPerFeatureByClusterID: HashMap[types.ClusterIDType.ClusterID, Array[Int]]

Number of occurences for each features by ClusterID

lazy val occurencesRatioPerFeatureByClusterID: HashMap[ClusterID, Array[Double]]

HashMap of each feature occurence ratio by clusterID, compared to global features

lazy val stdFrequencies: Array[Double]

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

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

final def wait(arg0: Long): Unit

final def wait(): Unit

def finalize(): Unit