org.apache.spark.mllib.fpm

Class FPGrowth

Object

org.apache.spark.mllib.fpm.FPGrowth

All Implemented Interfaces:

java.io.Serializable, org.apache.spark.internal.Logging

public class FPGrowth
extends Object
implements org.apache.spark.internal.Logging, scala.Serializable

A parallel FP-growth algorithm to mine frequent itemsets. The algorithm is described in Li et al., PFP: Parallel FP-Growth for Query Recommendation. PFP distributes computation in such a way that each worker executes an independent group of mining tasks. The FP-Growth algorithm is described in Han et al., Mining frequent patterns without candidate generation.

param: minSupport the minimal support level of the frequent pattern, any pattern that appears more than (minSupport * size-of-the-dataset) times will be output param: numPartitions number of partitions used by parallel FP-growth

See Also:

Association rule learning (Wikipedia)

, Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	FPGrowth.FreqItemset<Item> Frequent itemset.

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

org.apache.spark.internal.Logging.SparkShellLoggingFilter

Constructor Summary

Constructors

Constructor and Description
FPGrowth() Constructs a default instance with default parameters {minSupport: 0.3, numPartitions: same as the input data}.

Method Summary

Modifier and Type	Method and Description
<Item,Basket extends Iterable<Item>> FPGrowthModel<Item>	run(JavaRDD<Basket> data) Java-friendly version of run.
<Item> FPGrowthModel<Item>	run(RDD<Object> data, scala.reflect.ClassTag<Item> evidence$4) Computes an FP-Growth model that contains frequent itemsets.
FPGrowth	setMinSupport(double minSupport) Sets the minimal support level (default: 0.3).
FPGrowth	setNumPartitions(int numPartitions) Sets the number of partitions used by parallel FP-growth (default: same as input data).

Methods inherited from class Object

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

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

FPGrowth

public FPGrowth()

Constructs a default instance with default parameters {minSupport: 0.3, numPartitions: same as the input data}.

Method Detail

setMinSupport

public FPGrowth setMinSupport(double minSupport)

Sets the minimal support level (default: 0.3).

Parameters:

minSupport - (undocumented)

Returns:

(undocumented)

setNumPartitions

public FPGrowth setNumPartitions(int numPartitions)

Sets the number of partitions used by parallel FP-growth (default: same as input data).

Parameters:

numPartitions - (undocumented)

Returns:

(undocumented)

run

public <Item> FPGrowthModel<Item> run(RDD<Object> data,
                                      scala.reflect.ClassTag<Item> evidence$4)

Computes an FP-Growth model that contains frequent itemsets.

Parameters:

data - input data set, each element contains a transaction

evidence$4 - (undocumented)

Returns:

an FPGrowthModel

run

public <Item,Basket extends Iterable<Item>> FPGrowthModel<Item> run(JavaRDD<Basket> data)

Java-friendly version of run.

Parameters:

data - (undocumented)

Returns:

(undocumented)