MCL

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. final def eq(arg0: AnyRef): Boolean

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9. def equals(arg0: Any): Boolean

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10. def finalize(): Unit

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

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12. def hashCode(): Int

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13. final def isInstanceOf[T0]: Boolean

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15. final def notify(): Unit

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16. final def notifyAll(): Unit

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

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18. def toString(): String

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19. def train[VD](graph: Graph[VD, Double], expansionRate: Int = 2, inflationRate: Double = 2.0, convergenceRate: Double = 0.01, epsilon: Double = 0.01, maxIterations: Int = 10, selfLoopWeight: Double = 1, graphOrientationStrategy: String = "undirected"): MCLModel

    Train an MCL model using the given set of parameters.

    Train an MCL model using the given set of parameters.

    graph

    training points stored as BlockMatrix

    expansionRate

    expansion rate of adjacency matrix at each iteration

    inflationRate

    inflation rate of adjacency matrix at each iteration

    epsilon

    minimum percentage of a weight edge to be significant

    maxIterations

    maximal number of iterations for a non convergent algorithm

    selfLoopWeight

    a coefficient between 0 and 1 to influence clustering granularity and objective

    graphOrientationStrategy

    chose a graph strategy completion depending on its nature. 3 choices: undirected, directed, birected.

    returns

    an MCL object

20. final def wait(): Unit

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

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

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