FP Growth

Does Pattern Mining using FPGrowth Algorithm

Type

transform

Class

fire.nodes.ml.NodeFPGrowth

Fields

Name

Title

Description

itemsCol

Items Column

minSupport

Min Support

The minimum support for an itemset to be identified as frequent

minConfidence

Min Confidence

outputType

OutputType

Details

Mining frequent items, itemsets, subsequences, or other substructures is usually among the first steps to analyze a large-scale dataset, which has been an active research topic in data mining for years.

The FP-growth algorithm is described in the paper Han et al., Mining frequent patterns without candidate generation, where “FP” stands for frequent pattern. Given a dataset of transactions, the first step of FP-growth is to calculate item frequencies and identify frequent items. Different from Apriori-like algorithms designed for the same purpose, the second step of FP-growth uses a suffix tree (FP-tree) structure to encode transactions without generating candidate sets explicitly, which are usually expensive to generate. After the second step, the frequent itemsets can be extracted from the FP-tree. In spark.mllib, we implemented a parallel version of FP-growth called PFP, as described in Li et al., PFP: Parallel FP-growth for query recommendation. PFP distributes the work of growing FP-trees based on the suffixes of transactions, and hence is more scalable than a single-machine implementation. We refer users to the papers for more details.

spark.ml’s FP-growth implementation takes the following (hyper-)parameters:

  • minSupport: the minimum support for an itemset to be identified as frequent. For example, if an item appears 3 out of 5 transactions, it has a support of 3/5=0.6.

  • minConfidence: minimum confidence for generating Association Rule. Confidence is an indication of how often an association rule has been found to be true. For example, if in the transactions itemset X appears 4 times, X and Y co-occur only 2 times, the confidence for the rule X => Y is then 2/4 = 0.5. The parameter will not affect the mining for frequent itemsets, but specify the minimum confidence for generating association rules from frequent itemsets.

  • numPartitions: the number of partitions used to distribute the work. By default the param is not set, and number of partitions of the input dataset is used.

More details are available at Apache Spark ML docs page:

http://spark.apache.org/docs/latest/ml-frequent-pattern-mining.html

Examples

Below example is available at : https://spark.apache.org/docs/latest/ml-frequent-pattern-mining.html#fp-growth

import org.apache.spark.ml.fpm.FPGrowth

val dataset = spark.createDataset(Seq(

“1 2 5”,

“1 2 3 5”,

“1 2”)

).map(t => t.split(” “)).toDF(“items”)

val fpgrowth = new FPGrowth().setItemsCol(“items”).setMinSupport(0.5).setMinConfidence(0.6)

val model = fpgrowth.fit(dataset)

// Display frequent itemsets.

model.freqItemsets.show()

// Display generated association rules.

model.associationRules.show()

// transform examines the input items against all the association rules and summarize the

// consequents as prediction

model.transform(dataset).show()