Packages

class BisectingKMeans extends Estimator[BisectingKMeansModel] with BisectingKMeansParams with DefaultParamsWritable

A bisecting k-means algorithm based on the paper "A comparison of document clustering techniques" by Steinbach, Karypis, and Kumar, with modification to fit Spark. The algorithm starts from a single cluster that contains all points. Iteratively it finds divisible clusters on the bottom level and bisects each of them using k-means, until there are k leaf clusters in total or no leaf clusters are divisible. The bisecting steps of clusters on the same level are grouped together to increase parallelism. If bisecting all divisible clusters on the bottom level would result more than k leaf clusters, larger clusters get higher priority.

Annotations
@Since( "2.0.0" )
Source
BisectingKMeans.scala
See also

Steinbach, Karypis, and Kumar, A comparison of document clustering techniques, KDD Workshop on Text Mining, 2000.

Linear Supertypes
DefaultParamsWritable, MLWritable, BisectingKMeansParams, HasWeightCol, HasDistanceMeasure, HasPredictionCol, HasSeed, HasFeaturesCol, HasMaxIter, Estimator[BisectingKMeansModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. BisectingKMeans
  2. DefaultParamsWritable
  3. MLWritable
  4. BisectingKMeansParams
  5. HasWeightCol
  6. HasDistanceMeasure
  7. HasPredictionCol
  8. HasSeed
  9. HasFeaturesCol
  10. HasMaxIter
  11. Estimator
  12. PipelineStage
  13. Logging
  14. Params
  15. Serializable
  16. Serializable
  17. Identifiable
  18. AnyRef
  19. Any
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Visibility
  1. Public
  2. All

Parameters

A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.

  1. final val distanceMeasure: Param[String]

    Param for The distance measure.

    Param for The distance measure. Supported options: 'euclidean' and 'cosine'.

    Definition Classes
    HasDistanceMeasure
  2. final val featuresCol: Param[String]

    Param for features column name.

    Param for features column name.

    Definition Classes
    HasFeaturesCol
  3. final val k: IntParam

    The desired number of leaf clusters.

    The desired number of leaf clusters. Must be > 1. Default: 4. The actual number could be smaller if there are no divisible leaf clusters.

    Definition Classes
    BisectingKMeansParams
    Annotations
    @Since( "2.0.0" )
  4. final val maxIter: IntParam

    Param for maximum number of iterations (>= 0).

    Param for maximum number of iterations (>= 0).

    Definition Classes
    HasMaxIter
  5. final val predictionCol: Param[String]

    Param for prediction column name.

    Param for prediction column name.

    Definition Classes
    HasPredictionCol
  6. final val seed: LongParam

    Param for random seed.

    Param for random seed.

    Definition Classes
    HasSeed
  7. final val weightCol: Param[String]

    Param for weight column name.

    Param for weight column name. If this is not set or empty, we treat all instance weights as 1.0.

    Definition Classes
    HasWeightCol

Members

  1. final def clear(param: Param[_]): BisectingKMeans.this.type

    Clears the user-supplied value for the input param.

    Clears the user-supplied value for the input param.

    Definition Classes
    Params
  2. def copy(extra: ParamMap): BisectingKMeans

    Creates a copy of this instance with the same UID and some extra params.

    Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly. See defaultCopy().

    Definition Classes
    BisectingKMeans → Estimator → PipelineStage → Params
    Annotations
    @Since( "2.0.0" )
  3. def explainParam(param: Param[_]): String

    Explains a param.

    Explains a param.

    param

    input param, must belong to this instance.

    returns

    a string that contains the input param name, doc, and optionally its default value and the user-supplied value

    Definition Classes
    Params
  4. def explainParams(): String

    Explains all params of this instance.

    Explains all params of this instance. See explainParam().

    Definition Classes
    Params
  5. final def extractParamMap(): ParamMap

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  6. final def extractParamMap(extra: ParamMap): ParamMap

    Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.

    Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.

    Definition Classes
    Params
  7. def fit(dataset: Dataset[_]): BisectingKMeansModel

    Fits a model to the input data.

    Fits a model to the input data.

    Definition Classes
    BisectingKMeans → Estimator
    Annotations
    @Since( "2.0.0" )
  8. def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[BisectingKMeansModel]

    Fits multiple models to the input data with multiple sets of parameters.

    Fits multiple models to the input data with multiple sets of parameters. The default implementation uses a for loop on each parameter map. Subclasses could override this to optimize multi-model training.

    dataset

    input dataset

    paramMaps

    An array of parameter maps. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted models, matching the input parameter maps

    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  9. def fit(dataset: Dataset[_], paramMap: ParamMap): BisectingKMeansModel

    Fits a single model to the input data with provided parameter map.

    Fits a single model to the input data with provided parameter map.

    dataset

    input dataset

    paramMap

    Parameter map. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted model

    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  10. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): BisectingKMeansModel

    Fits a single model to the input data with optional parameters.

    Fits a single model to the input data with optional parameters.

    dataset

    input dataset

    firstParamPair

    the first param pair, overrides embedded params

    otherParamPairs

    other param pairs. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted model

    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  11. final def get[T](param: Param[T]): Option[T]

    Optionally returns the user-supplied value of a param.

    Optionally returns the user-supplied value of a param.

    Definition Classes
    Params
  12. final def getDefault[T](param: Param[T]): Option[T]

    Gets the default value of a parameter.

    Gets the default value of a parameter.

    Definition Classes
    Params
  13. final def getOrDefault[T](param: Param[T]): T

    Gets the value of a param in the embedded param map or its default value.

    Gets the value of a param in the embedded param map or its default value. Throws an exception if neither is set.

    Definition Classes
    Params
  14. def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  15. final def hasDefault[T](param: Param[T]): Boolean

    Tests whether the input param has a default value set.

    Tests whether the input param has a default value set.

    Definition Classes
    Params
  16. def hasParam(paramName: String): Boolean

    Tests whether this instance contains a param with a given name.

    Tests whether this instance contains a param with a given name.

    Definition Classes
    Params
  17. final def isDefined(param: Param[_]): Boolean

    Checks whether a param is explicitly set or has a default value.

    Checks whether a param is explicitly set or has a default value.

    Definition Classes
    Params
  18. final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  19. lazy val params: Array[Param[_]]

    Returns all params sorted by their names.

    Returns all params sorted by their names. The default implementation uses Java reflection to list all public methods that have no arguments and return Param.

    Definition Classes
    Params
    Note

    Developer should not use this method in constructor because we cannot guarantee that this variable gets initialized before other params.

  20. def save(path: String): Unit

    Saves this ML instance to the input path, a shortcut of write.save(path).

    Saves this ML instance to the input path, a shortcut of write.save(path).

    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  21. final def set[T](param: Param[T], value: T): BisectingKMeans.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes
    Params
  22. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  23. def transformSchema(schema: StructType): StructType

    Check transform validity and derive the output schema from the input schema.

    Check transform validity and derive the output schema from the input schema.

    We check validity for interactions between parameters during transformSchema and raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled by Param.validate().

    Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.

    Definition Classes
    BisectingKMeans → PipelineStage
    Annotations
    @Since( "2.0.0" )
  24. val uid: String

    An immutable unique ID for the object and its derivatives.

    An immutable unique ID for the object and its derivatives.

    Definition Classes
    BisectingKMeans → Identifiable
    Annotations
    @Since( "2.0.0" )
  25. def write: MLWriter

    Returns an MLWriter instance for this ML instance.

    Returns an MLWriter instance for this ML instance.

    Definition Classes
    DefaultParamsWritable → MLWritable

Parameter setters

  1. def setFeaturesCol(value: String): BisectingKMeans.this.type

    Annotations
    @Since( "2.0.0" )
  2. def setK(value: Int): BisectingKMeans.this.type

    Annotations
    @Since( "2.0.0" )
  3. def setMaxIter(value: Int): BisectingKMeans.this.type

    Annotations
    @Since( "2.0.0" )
  4. def setPredictionCol(value: String): BisectingKMeans.this.type

    Annotations
    @Since( "2.0.0" )
  5. def setSeed(value: Long): BisectingKMeans.this.type

    Annotations
    @Since( "2.0.0" )
  6. def setWeightCol(value: String): BisectingKMeans.this.type

    Sets the value of param weightCol.

    Sets the value of param weightCol. If this is not set or empty, we treat all instance weights as 1.0. Default is not set, so all instances have weight one.

    Annotations
    @Since( "3.0.0" )

Parameter getters

  1. final def getDistanceMeasure: String

    Definition Classes
    HasDistanceMeasure
  2. final def getFeaturesCol: String

    Definition Classes
    HasFeaturesCol
  3. def getK: Int

    Definition Classes
    BisectingKMeansParams
    Annotations
    @Since( "2.0.0" )
  4. final def getMaxIter: Int

    Definition Classes
    HasMaxIter
  5. final def getPredictionCol: String

    Definition Classes
    HasPredictionCol
  6. final def getSeed: Long

    Definition Classes
    HasSeed
  7. final def getWeightCol: String

    Definition Classes
    HasWeightCol

(expert-only) Parameters

A list of advanced, expert-only (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.

  1. final val minDivisibleClusterSize: DoubleParam

    The minimum number of points (if greater than or equal to 1.0) or the minimum proportion of points (if less than 1.0) of a divisible cluster (default: 1.0).

    The minimum number of points (if greater than or equal to 1.0) or the minimum proportion of points (if less than 1.0) of a divisible cluster (default: 1.0).

    Definition Classes
    BisectingKMeansParams
    Annotations
    @Since( "2.0.0" )

(expert-only) Parameter setters

  1. def setDistanceMeasure(value: String): BisectingKMeans.this.type

    Annotations
    @Since( "2.4.0" )
  2. def setMinDivisibleClusterSize(value: Double): BisectingKMeans.this.type

    Annotations
    @Since( "2.0.0" )

(expert-only) Parameter getters

  1. def getMinDivisibleClusterSize: Double

    Definition Classes
    BisectingKMeansParams
    Annotations
    @Since( "2.0.0" )