DbscanSettings

Value Members

1. final def !=(arg0: AnyRef): Boolean

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2. final def !=(arg0: Any): Boolean

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3. final def ##(): Int

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4. final def ==(arg0: AnyRef): Boolean

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5. final def ==(arg0: Any): Boolean

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6. final def asInstanceOf[T0]: T0

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7. def clone(): AnyRef

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8. def distanceMeasure: DistanceMeasure

   A distance measure

   A distance measure

   returns

9. def epsilon: Double

   Distance within which points are considered close enough to be assigned to one cluster

   Distance within which points are considered close enough to be assigned to one cluster

   returns

10. final def eq(arg0: AnyRef): Boolean

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11. def equals(arg0: Any): Boolean

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12. def finalize(): Unit

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13. final def getClass(): Class[_]

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14. def hashCode(): Int

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15. final def isInstanceOf[T0]: Boolean

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16. final def ne(arg0: AnyRef): Boolean

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17. final def notify(): Unit

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18. final def notifyAll(): Unit

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19. def numberOfPoints: Int

    Minimal number of points within a distance specified by epsilon which is enough to start a new cluster

    Minimal number of points within a distance specified by epsilon which is enough to start a new cluster

    returns

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

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21. def toString(): String

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22. def treatBorderPointsAsNoise: Boolean

    A flag which indicates whether border points of clusters should be treated as noise

    A flag which indicates whether border points of clusters should be treated as noise

    returns

23. final def wait(): Unit

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

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25. final def wait(arg0: Long): Unit

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26. def withDistanceMeasure(dm: DistanceMeasure): DbscanSettings

    Sets a distance measure

    Sets a distance measure

    dm

    An object which implements the org.apache.commons.math3.ml.distance.DistanceMeasure interface

    returns

    This DbscanSettings object with modified distance measure

27. def withEpsilon(eps: Double): DbscanSettings

    Set epsilon parameter of the algorithm (distance within which points are considered close enough to be assigned to one cluster)

    Set epsilon parameter of the algorithm (distance within which points are considered close enough to be assigned to one cluster)

    eps

    returns

28. def withNumberOfPoints(np: Int): DbscanSettings

    Sets minimal number of points within a distance specified by epsilon which is enough to start a new cluster

    Sets minimal number of points within a distance specified by epsilon which is enough to start a new cluster

    np

    returns

29. def withTreatBorderPointsAsNoise(tbpn: Boolean): DbscanSettings

    Sets a flag which indicates whether border points should be treated as noise

    Sets a flag which indicates whether border points should be treated as noise

    tbpn

    returns