FP-Growth (RapidMiner Studio Core)

Description

In simple words, frequent itemsets are groups of items that often appear together in the data. It is important to know the basics of market-basket analysis for understanding frequent itemsets.

The market-basket model of data is used to describe a common form of a many-to-many relationship between two kinds of objects. On the one hand, we have items, and on the other we have baskets, also called 'transactions'. The set of items is usually represented as set of attributes. Mostly these attributes are binominal. The transactions are usually each represented as examples of the ExampleSet. When an attribute value is 'true' in an example; it implies that the corresponding item is present in that transaction. Each transaction consists of a set of items (an itemset). Usually it is assumed that the number of items in a transaction is small, much smaller than the total number of items i.e. in most of the examples most of the attribute values are 'false'. The number of transactions is usually assumed to be very large i.e. the number of examples in the ExampleSet is assumed to be large. The frequent-itemsets problem is that of finding sets of items that appear together in at least a threshold ratio of transactions. This threshold is defined by the 'minimum support' criteria. The support of an itemset is the number of times that itemset appears in the ExampleSet divided by the total number of examples. The 'Purchases' data set at "Samples/data/Purchases" in the repository of RapidMiner is an example of how transactions data usually look like.

The discovery of frequent itemsets is often viewed as the discovery of 'association rules', although the latter is a more complex characterization of data, whose discovery depends fundamentally on the discovery of frequent itemsets. Association rules are derived from the frequent itemsets. The FP-Growth operator finds the frequent itemsets and operators like the Create Association Rules operator uses these frequent itemsets for calculating the association rules.

This operator calculates all frequent itemsets from an ExampleSet by building a FP-tree data structure on the transaction data base. This is a very compressed copy of the data which in many cases fits into main memory even for large data bases. All frequent itemsets are derived from this FP-tree. Many other frequent itemset mining algorithms also exist e.g. the Apriori algorithm. A major advantage of FP-Growth compared to Apriori is that it uses only 2 data scans and is therefore often applicable even on large data sets.

Please note that the given ExampleSet should contain only binominal attributes, i.e. nominal attributes with only two different values. If your ExampleSet does not satisfy this condition, you may use appropriate preprocessing operators to transform it into the required form. The discretization operators can be used for changing the value of numerical attributes to nominal attributes. Then the Nominal to Binominal operator can be used for transforming nominal attributes into binominal attributes.

Please note that the frequent itemsets are mined for the positive entries in your ExampleSet, i.e. for those nominal values which are defined as positive in your ExampleSet. If your data does not specify the positive entries correctly, you may set them using the positive value parameter. This only works if all your attributes contain this value.

This operator has two basic working modes:

• finding at least the specified number of itemsets with highest support without taking the 'min support' into account. This mode is available when the find min number of itemsets parameter is set to true. Then this operator finds the number of itemsets specified in the min number of itemsets parameter. The min support parameter is ignored in this case.
• finding all itemsets with a support larger than the specified minimum support. The minimum support is specified through the min support parameter. This mode is available when the find min number of itemsets parameter is set to false.