org.apache.spark.ml.clustering

Interface KMeansParams

All Superinterfaces:

HasDistanceMeasure, HasFeaturesCol, HasMaxBlockSizeInMB, HasMaxIter, HasPredictionCol, HasSeed, HasSolver, HasTol, HasWeightCol, Identifiable, Params, java.io.Serializable

All Known Implementing Classes:

KMeans, KMeansModel

public interface KMeansParams
extends Params, HasMaxIter, HasFeaturesCol, HasSeed, HasPredictionCol, HasTol, HasDistanceMeasure, HasWeightCol, HasSolver, HasMaxBlockSizeInMB

Common params for KMeans and KMeansModel

Method Summary

Modifier and Type	Method and Description
String	getInitMode()
int	getInitSteps()
int	getK()
Param<String>	initMode() Param for the initialization algorithm.
IntParam	initSteps() Param for the number of steps for the k-means|| initialization mode.
IntParam	k() The number of clusters to create (k).
Param<String>	solver() Param for the name of optimization method used in KMeans.
StructType	validateAndTransformSchema(StructType schema) Validates and transforms the input schema.

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

getMaxIter, maxIter

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

featuresCol, getFeaturesCol

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

getSeed, seed

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

getPredictionCol, predictionCol

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

getTol, tol

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

distanceMeasure, getDistanceMeasure

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

getWeightCol, weightCol

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

getSolver

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

getMaxBlockSizeInMB, maxBlockSizeInMB

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

clear, copy, 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, uid

Method Detail

getInitMode

String getInitMode()

getInitSteps

int getInitSteps()

getK

int getK()

initMode

Param<String> initMode()

Param for the initialization algorithm. This can be either "random" to choose random points as initial cluster centers, or "k-means||" to use a parallel variant of k-means++ (Bahmani et al., Scalable K-Means++, VLDB 2012). Default: k-means||.

Returns:

(undocumented)

initSteps

IntParam initSteps()

Param for the number of steps for the k-means|| initialization mode. This is an advanced setting -- the default of 2 is almost always enough. Must be > 0. Default: 2.

Returns:

(undocumented)

k

IntParam k()

The number of clusters to create (k). Must be > 1. Note that it is possible for fewer than k clusters to be returned, for example, if there are fewer than k distinct points to cluster. Default: 2.

Returns:

(undocumented)

solver

Param<String> solver()

Param for the name of optimization method used in KMeans. Supported options: - "auto": Automatically select the solver based on the input schema and sparsity: If input instances are arrays or input vectors are dense, set to "block". Else, set to "row". - "row": input instances are processed row by row, and triangle-inequality is applied to accelerate the training. - "block": input instances are stacked to blocks, and GEMM is applied to compute the distances. Default is "auto".

Specified by:

solver in interface HasSolver

Returns:

(undocumented)

validateAndTransformSchema

StructType validateAndTransformSchema(StructType schema)

Validates and transforms the input schema.

Parameters:

schema - input schema

Returns:

output schema