org.apache.spark.mllib.fpm

Class FPGrowth

java.lang.Object

org.apache.spark.mllib.fpm.FPGrowth

All Implemented Interfaces:

java.io.Serializable, Logging

public class FPGrowth
extends java.lang.Object
implements 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 appears more than (minSupport * size-of-the-dataset) times will be output param: numPartitions number of partitions used by parallel FP-growth

See Also:

http://en.wikipedia.org/wiki/Association_rule_learning Association rule learning (Wikipedia)}

, Serialized Form

Nested Class Summary

Nested Classes

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

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

Methods

Modifier and Type	Method and Description
<Item,Basket extends java.lang.Iterable<Item>> FPGrowthModel<Item>	run(JavaRDD<Basket> data) Java-friendly version of run.
<Item> FPGrowthModel<Item>	run(RDD<java.lang.Object> data, scala.reflect.ClassTag<Item> evidence$2) 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 java.lang.Object

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

Methods inherited from interface org.apache.spark.Logging

initializeIfNecessary, initializeLogging, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning

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<java.lang.Object> data,
                             scala.reflect.ClassTag<Item> evidence$2)

Computes an FP-Growth model that contains frequent itemsets.

Parameters:

data - input data set, each element contains a transaction

evidence$2 - (undocumented)

Returns:

an FPGrowthModel

run

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

Java-friendly version of run.