org.apache.spark.ml.clustering

Class BisectingKMeans

Object

org.apache.spark.ml.PipelineStage

org.apache.spark.ml.Estimator<BisectingKMeansModel>

org.apache.spark.ml.clustering.BisectingKMeans

All Implemented Interfaces:

java.io.Serializable, org.apache.spark.internal.Logging, BisectingKMeansParams, Params, HasDistanceMeasure, HasFeaturesCol, HasMaxIter, HasPredictionCol, HasSeed, HasWeightCol, DefaultParamsWritable, Identifiable, MLWritable

public class BisectingKMeans
extends Estimator<BisectingKMeansModel>
implements BisectingKMeansParams, 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.

See Also:

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

Nested Class Summary

Nested classes/interfaces inherited from interface org.apache.spark.internal.Logging

org.apache.spark.internal.Logging.SparkShellLoggingFilter

Constructor Summary

Constructors

Constructor and Description
BisectingKMeans()
BisectingKMeans(String uid)

Method Summary

Modifier and Type	Method and Description
BisectingKMeans	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
Param<String>	distanceMeasure() Param for The distance measure.
Param<String>	featuresCol() Param for features column name.
BisectingKMeansModel	fit(Dataset<?> dataset) Fits a model to the input data.
IntParam	k() The desired number of leaf clusters.
static BisectingKMeans	load(String path)
IntParam	maxIter() Param for maximum number of iterations (&gt;= 0).
DoubleParam	minDivisibleClusterSize() 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).
Param<String>	predictionCol() Param for prediction column name.
static MLReader<T>	read()
LongParam	seed() Param for random seed.
BisectingKMeans	setDistanceMeasure(String value)
BisectingKMeans	setFeaturesCol(String value)
BisectingKMeans	setK(int value)
BisectingKMeans	setMaxIter(int value)
BisectingKMeans	setMinDivisibleClusterSize(double value)
BisectingKMeans	setPredictionCol(String value)
BisectingKMeans	setSeed(long value)
BisectingKMeans	setWeightCol(String value) Sets the value of param weightCol.
StructType	transformSchema(StructType schema) Check transform validity and derive the output schema from the input schema.
String	uid() An immutable unique ID for the object and its derivatives.
Param<String>	weightCol() Param for weight column name.

Methods inherited from class org.apache.spark.ml.Estimator

fit, fit, fit, fit

Methods inherited from class org.apache.spark.ml.PipelineStage

params

Methods inherited from class Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.apache.spark.ml.clustering.BisectingKMeansParams

getK, getMinDivisibleClusterSize, validateAndTransformSchema

Methods inherited from interface org.apache.spark.ml.param.shared.HasMaxIter

getMaxIter

Methods inherited from interface org.apache.spark.ml.param.shared.HasFeaturesCol

getFeaturesCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasSeed

getSeed

Methods inherited from interface org.apache.spark.ml.param.shared.HasPredictionCol

getPredictionCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasDistanceMeasure

getDistanceMeasure

Methods inherited from interface org.apache.spark.ml.param.shared.HasWeightCol

getWeightCol

Methods inherited from interface org.apache.spark.ml.param.Params

clear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, onParamChange, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn

Methods inherited from interface org.apache.spark.ml.util.Identifiable

toString

Methods inherited from interface org.apache.spark.ml.util.DefaultParamsWritable

write

Methods inherited from interface org.apache.spark.ml.util.MLWritable

save

Methods inherited from interface org.apache.spark.internal.Logging

$init$, initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, initLock, isTraceEnabled, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning, org$apache$spark$internal$Logging$$log__$eq, org$apache$spark$internal$Logging$$log_, uninitialize

Constructor Detail

BisectingKMeans

public BisectingKMeans(String uid)

BisectingKMeans

public BisectingKMeans()

Method Detail

load

public static BisectingKMeans load(String path)

read

public static MLReader<T> read()

k

public final IntParam k()

Description copied from interface: BisectingKMeansParams

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

Specified by:

k in interface BisectingKMeansParams

Returns:

(undocumented)

minDivisibleClusterSize

public final DoubleParam minDivisibleClusterSize()

Description copied from interface: BisectingKMeansParams

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).

Specified by:

minDivisibleClusterSize in interface BisectingKMeansParams

Returns:

(undocumented)

weightCol

public final Param<String> weightCol()

Description copied from interface: HasWeightCol

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

Specified by:

weightCol in interface HasWeightCol

Returns:

(undocumented)

distanceMeasure

public final Param<String> distanceMeasure()

Description copied from interface: HasDistanceMeasure

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

Specified by:

distanceMeasure in interface HasDistanceMeasure

Returns:

(undocumented)

predictionCol

public final Param<String> predictionCol()

Description copied from interface: HasPredictionCol

Param for prediction column name.

Specified by:

predictionCol in interface HasPredictionCol

Returns:

(undocumented)

seed

public final LongParam seed()

Description copied from interface: HasSeed

Param for random seed.

Specified by:

seed in interface HasSeed

Returns:

(undocumented)

featuresCol

public final Param<String> featuresCol()

Description copied from interface: HasFeaturesCol

Param for features column name.

Specified by:

featuresCol in interface HasFeaturesCol

Returns:

(undocumented)

maxIter

public final IntParam maxIter()

Description copied from interface: HasMaxIter

Param for maximum number of iterations (&gt;= 0).

Specified by:

maxIter in interface HasMaxIter

Returns:

(undocumented)

uid

public String uid()

Description copied from interface: Identifiable

An immutable unique ID for the object and its derivatives.

Specified by:

uid in interface Identifiable

Returns:

(undocumented)

copy

public BisectingKMeans copy(ParamMap extra)

Description copied from interface: 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().

Specified by:

copy in interface Params

Specified by:

copy in class Estimator<BisectingKMeansModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

setFeaturesCol

public BisectingKMeans setFeaturesCol(String value)

setPredictionCol

public BisectingKMeans setPredictionCol(String value)

setK

public BisectingKMeans setK(int value)

setMaxIter

public BisectingKMeans setMaxIter(int value)

setSeed

public BisectingKMeans setSeed(long value)

setMinDivisibleClusterSize

public BisectingKMeans setMinDivisibleClusterSize(double value)

setDistanceMeasure

public BisectingKMeans setDistanceMeasure(String value)

setWeightCol

public BisectingKMeans setWeightCol(String value)

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.

Parameters:

value - (undocumented)

Returns:

(undocumented)

fit

public BisectingKMeansModel fit(Dataset<?> dataset)

Description copied from class: Estimator

Fits a model to the input data.

Specified by:

fit in class Estimator<BisectingKMeansModel>

Parameters:

dataset - (undocumented)

Returns:

(undocumented)

transformSchema

public StructType transformSchema(StructType schema)

Description copied from class: PipelineStage

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.

Specified by:

transformSchema in class PipelineStage

Parameters:

schema - (undocumented)

Returns:

(undocumented)