Packages

  • package root
    Definition Classes
    root
  • package org
    Definition Classes
    root
  • package apache
    Definition Classes
    org
  • package spark

    Core Spark functionality.

    Core Spark functionality. org.apache.spark.SparkContext serves as the main entry point to Spark, while org.apache.spark.rdd.RDD is the data type representing a distributed collection, and provides most parallel operations.

    In addition, org.apache.spark.rdd.PairRDDFunctions contains operations available only on RDDs of key-value pairs, such as groupByKey and join; org.apache.spark.rdd.DoubleRDDFunctions contains operations available only on RDDs of Doubles; and org.apache.spark.rdd.SequenceFileRDDFunctions contains operations available on RDDs that can be saved as SequenceFiles. These operations are automatically available on any RDD of the right type (e.g. RDD[(Int, Int)] through implicit conversions.

    Java programmers should reference the org.apache.spark.api.java package for Spark programming APIs in Java.

    Classes and methods marked with Experimental are user-facing features which have not been officially adopted by the Spark project. These are subject to change or removal in minor releases.

    Classes and methods marked with Developer API are intended for advanced users want to extend Spark through lower level interfaces. These are subject to changes or removal in minor releases.

    Definition Classes
    apache
  • package mllib

    RDD-based machine learning APIs (in maintenance mode).

    RDD-based machine learning APIs (in maintenance mode).

    The spark.mllib package is in maintenance mode as of the Spark 2.0.0 release to encourage migration to the DataFrame-based APIs under the org.apache.spark.ml package. While in maintenance mode,

    • no new features in the RDD-based spark.mllib package will be accepted, unless they block implementing new features in the DataFrame-based spark.ml package;
    • bug fixes in the RDD-based APIs will still be accepted.

    The developers will continue adding more features to the DataFrame-based APIs in the 2.x series to reach feature parity with the RDD-based APIs. And once we reach feature parity, this package will be deprecated.

    Definition Classes
    spark
    See also

    SPARK-4591 to track the progress of feature parity

  • package fpm
    Definition Classes
    mllib
  • AssociationRules
  • FPGrowth
  • FPGrowthModel
  • PrefixSpan
  • PrefixSpanModel

class PrefixSpan extends Logging with Serializable

A parallel PrefixSpan algorithm to mine frequent sequential patterns. The PrefixSpan algorithm is described in J. Pei, et al., PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth (see here).

Annotations
@Since( "1.5.0" )
See also

Sequential Pattern Mining (Wikipedia)

Linear Supertypes
Serializable, Serializable, Logging, AnyRef, Any
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Inherited
  1. PrefixSpan
  2. Serializable
  3. Serializable
  4. Logging
  5. AnyRef
  6. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new PrefixSpan()

    Constructs a default instance with default parameters {minSupport: 0.1, maxPatternLength: 10, maxLocalProjDBSize: 32000000L}.

    Constructs a default instance with default parameters {minSupport: 0.1, maxPatternLength: 10, maxLocalProjDBSize: 32000000L}.

    Annotations
    @Since( "1.5.0" )

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  5. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  6. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  7. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  8. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  9. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  10. def getMaxLocalProjDBSize: Long

    Gets the maximum number of items allowed in a projected database before local processing.

    Gets the maximum number of items allowed in a projected database before local processing.

    Annotations
    @Since( "1.5.0" )
  11. def getMaxPatternLength: Int

    Gets the maximal pattern length (i.e.

    Gets the maximal pattern length (i.e. the length of the longest sequential pattern to consider.

    Annotations
    @Since( "1.5.0" )
  12. def getMinSupport: Double

    Get the minimal support (i.e.

    Get the minimal support (i.e. the frequency of occurrence before a pattern is considered frequent).

    Annotations
    @Since( "1.5.0" )
  13. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  14. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean = false): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  15. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  16. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  17. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  18. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  19. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  20. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  21. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  22. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  23. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  24. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  25. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  26. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  27. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  28. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  29. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  30. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  31. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  32. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  33. def run[Item, Itemset <: Iterable[Item], Sequence <: Iterable[Itemset]](data: JavaRDD[Sequence]): PrefixSpanModel[Item]

    A Java-friendly version of run() that reads sequences from a JavaRDD and returns frequent sequences in a PrefixSpanModel.

    A Java-friendly version of run() that reads sequences from a JavaRDD and returns frequent sequences in a PrefixSpanModel.

    Item

    item type

    Itemset

    itemset type, which is an Iterable of Items

    Sequence

    sequence type, which is an Iterable of Itemsets

    data

    ordered sequences of itemsets stored as Java Iterable of Iterables

    returns

    a PrefixSpanModel that contains the frequent sequential patterns

    Annotations
    @Since( "1.5.0" )
  34. def run[Item](data: RDD[Array[Array[Item]]])(implicit arg0: ClassTag[Item]): PrefixSpanModel[Item]

    Finds the complete set of frequent sequential patterns in the input sequences of itemsets.

    Finds the complete set of frequent sequential patterns in the input sequences of itemsets.

    data

    sequences of itemsets.

    returns

    a PrefixSpanModel that contains the frequent patterns

    Annotations
    @Since( "1.5.0" )
  35. def setMaxLocalProjDBSize(maxLocalProjDBSize: Long): PrefixSpan.this.type

    Sets the maximum number of items (including delimiters used in the internal storage format) allowed in a projected database before local processing (default: 32000000L).

    Sets the maximum number of items (including delimiters used in the internal storage format) allowed in a projected database before local processing (default: 32000000L).

    Annotations
    @Since( "1.5.0" )
  36. def setMaxPatternLength(maxPatternLength: Int): PrefixSpan.this.type

    Sets maximal pattern length (default: 10).

    Sets maximal pattern length (default: 10).

    Annotations
    @Since( "1.5.0" )
  37. def setMinSupport(minSupport: Double): PrefixSpan.this.type

    Sets the minimal support level (default: 0.1).

    Sets the minimal support level (default: 0.1).

    Annotations
    @Since( "1.5.0" )
  38. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  39. def toString(): String
    Definition Classes
    AnyRef → Any
  40. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  41. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  42. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )

Inherited from Serializable

Inherited from Serializable

Inherited from Logging

Inherited from AnyRef

Inherited from Any

Ungrouped