Github user hhbyyh commented on a diff in the pull request: https://github.com/apache/spark/pull/17130#discussion_r108600468 --- Diff: docs/ml-frequent-pattern-mining.md --- @@ -0,0 +1,75 @@ +--- +layout: global +title: Frequent Pattern Mining +displayTitle: Frequent Pattern Mining +--- + +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. +We refer users to Wikipedia's [association rule learning](http://en.wikipedia.org/wiki/Association_rule_learning) +for more information. + +**Table of Contents** + +* This will become a table of contents (this text will be scraped). +{:toc} + +## FP-Growth + +The FP-growth algorithm is described in the paper +[Han et al., Mining frequent patterns without candidate generation](http://dx.doi.org/10.1145/335191.335372), +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](http://en.wikipedia.org/wiki/Apriori_algorithm) 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](http://dx.doi.org/10.1145/1454008.1454027). +PFP distributes the work of growing FP-trees based on the suffices of transactions, +and hence 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. 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 + partition number of the input dataset is used. + +The `FPGrowthModel` provides: + +* `freqItemsets`: frequent itemsets in the format of DataFrame("items"[Array], "freq"[Long]) +* `associationRules`: association rules generated with confidence above `minConfidence`, in the format of + DataFrame("antecedent"[Array], "consequent"[Array], "confidence"[Double]). +* `transform`: The transform method examines the input items in `itemsCol` against all the association rules and + summarize the consequents as prediction. The prediction column has the same data type as the --- End diff -- Thanks for the suggestion. I do wish to have a better illustration here. But the two containing in your version make it not that straightforward, and actually it should be items in `itemsCol ` contains the antecedents for association rules. I extend it to a longer version, For each record in `itemsCol`, the `transform` method will compare its items against the antecedents of each association rule. If the record contains all the antecedents of a specific association rule, the rule will be considered as applicable and its consequents will be added to the prediction result. The `transform` method will summarize the consequents from all the applicable rules as prediction.
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