Class/Object

net.sansa_stack.ml.spark.clustering.algorithms

Kmeans

Related Docs: object Kmeans | package algorithms

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class Kmeans extends ClusterAlgo

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ClusterAlgo, AnyRef, Any
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Instance Constructors

  1. new Kmeans(input: RDD[Triple])

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Value Members

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

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  2. final def ##(): Int

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

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  4. final def asInstanceOf[T0]: T0

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  5. def clone(): AnyRef

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  6. val conf: Config

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  7. val dataProcessing: DataProcessing

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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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  14. def kmClustering(numClusters: Int, maxIter: Int, df: DataFrame, spark: SparkSession): Map[Int, Array[Long]]

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    K-means clustering based on given Dataframe

    K-means clustering based on given Dataframe

    returns

    cluster id and corresponding pois in cluster

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

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  16. var noOfIter: Int

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  17. var noofcluster: Int

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  18. final def notify(): Unit

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  19. final def notifyAll(): Unit

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  20. var oneHotClusters: Map[Int, Array[Long]]

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  21. val pois: RDD[POI]

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  22. def run(): RDD[(Int, List[Triple])]

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  23. def setK(K: Int): Kmeans.this.type

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  24. def setMaxIterations(iter: Int): Kmeans.this.type

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    set maximum iterations for Kmeans,PIC etc

  25. val spark: SparkSession

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

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  27. def toString(): String

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  28. final def wait(): Unit

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

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

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Inherited from ClusterAlgo

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