Mining Frequent Itemsets using the FP-Growth Algorithm (SPMF documentation)

This example explains how to run the FP-Growth algorithm using the SPMF open-source data mining library.

How to run this example?

What is FPGrowth?

FPGrowth is an algorithm for discovering frequent itemsets in a transaction database. It was proposed by Han et al. (2000). FPGrowth is a very fast and memory efficient algorithm. It uses a special internal structure called an FP-Tree.

What is the input of the FPGrowth algorithm?

The input of FPGrowth is a transaction database (aka binary context) and a threshold named minsup (a value between 0 and 100 %).

A transaction database is a set of transactions. Each transaction is a set of items. For example, consider the following transaction database. It contains 5 transactions (t1, t2, ..., t5) and 5 items (1,2, 3, 4, 5). For example, the first transaction represents the set of items 1, 3 and 4. This database is provided as the file contextPasquier99.txt in the SPMF distribution. It is important to note that an item is not allowed to appear twice in the same transaction and that items are assumed to be sorted by lexicographical order in a transaction.

Transaction id Items
t1 {1, 3, 4}
t2 {2, 3, 5}
t3 {1, 2, 3, 5}
t4 {2, 5}
t5 {1, 2, 3, 5}

What is the output of the FPGrowth algorithm?

FPGrowth is an algorithm for discovering itemsets (group of items) occurring frequently in a transaction database (frequent itemsets). A frequent itemset is an itemset appearing in at least minsup transactions from the transaction database, where minsup is a parameter given by the user.

For example, if FPGrowth is run on the previous transaction database with a minsup of 40 % (2 transactions), FPGrowth produces the following result:

itemsets support
{1} 3
{2} 4
{3} 4
{5} 4
{1, 2} 2
{1, 3} 3
{1, 5} 2
{2, 3} 3
{2, 5} 4
{3, 5} 3
{1, 2, 3} 2
{1, 2, 5} 2
{1, 3, 5} 2
{2, 3, 5} 3
{1, 2, 3, 5} 2

How should I interpret the results?

In the results, each itemset is annotated with its support. The support of an itemset is how many times the itemset appears in the transaction database. For example, the itemset {2, 3 5} has a support of 3 because it appears in transactions t2, t3 and t5. It is a frequent itemset because its support is higher or equal to the minsup parameter.

Input file format

The input file format used by FPGrowth is defined as follows. It is a text file. An item is represented by a positive integer. A transaction is a line in the text file. In each line (transaction), items are separated by a single space. It is assumed that all items within a same transaction (line) are sorted according to a total order (e.g. ascending order) and that no item can appear twice within the same line.

For example, for the previous example, the input file is defined as follows:

1 3 4
2 3 5
1 2 3 5
2 5
1 2 3 5

Note that it is also possible to use the ARFF format as an alternative to the default input format. The specification of the ARFF format can be found here. Most features of the ARFF format are supported except that (1) the character "=" is forbidden and (2) escape characters are not considered. Note that when the ARFF format is used, the performance of the data mining algorithms will be slightly less than if the native SPMF file format is used because a conversion of the input file will be automatically performed before launching the algorithm and the result will also have to be converted. This cost however should be small.

Output file format

The output file format is defined as follows. It is a text file, where each line represents a frequent itemset. On each line, the items of the itemset are first listed. Each item is represented by an integer and it is followed by a single space. After, all the items, the keyword "#SUP:" appears, which is followed by an integer indicating the support of the itemset, expressed as a number of transactions. For example, here is the output file for this example. The first line indicates the frequent itemset consisting of the item 1 and it indicates that this itemset has a support of 3 transactions.

1 #SUP: 3
2 #SUP: 4
3 #SUP: 4
5 #SUP: 4
1 2 #SUP: 2
1 3 #SUP: 3
1 5 #SUP: 2
2 3 #SUP: 3
2 5 #SUP: 4
3 5 #SUP: 3
1 2 3 #SUP: 2
1 2 5 #SUP: 2
1 3 5 #SUP: 2
2 3 5 #SUP: 3
1 2 3 5 #SUP: 2

Note that if the ARFF format is used as input instead of the default input format, the output format will be the same except that items will be represented by strings instead of integers.

Optional feature: giving names to items

Some users have requested the feature of given names to items instead of using numbers. This feature is offered in the user interface of SPMF and in the command line of SPMF. To use this feature, your file must include @CONVERTED_FROM_TEXT as first line and then several lines to define the names of items in your file. For example, consider the example database "contextPasquier99.txt". Here we have modified the file to give names to the items: 

@CONVERTED_FROM_TEXT
@ITEM=1=apple
@ITEM=2=orange
@ITEM=3=tomato
@ITEM=4=milk
@ITEM=5=bread
1 3 4
2 3 5
1 2 3 5
2 5
1 2 3 5

In this file, the first line indicates, that it is a file where names are given to items. Then, the second line indicates that the item 1 is called "apple". The third line indicates that the item 2 is called "orange". Then the following lines define transactions in the SPMF format.

Then, if we apply the algorithm using this file using the user interface of SPMF or the command line, the output file contains several patterns, including the following ones:

orange tomato bread #SUP: 3
orange bread #SUP: 4
apple orange tomato bread #SUP: 2

Note that this feature could be also used from the source code of SPMF using the ResultConverter class. However, there is currently no example provided for using it from the source code.

Optional feature: constraints on the size of itemsets

Sometimes, there may be just too many itemsets, and itemsets containing many items may not be interesting. Thus, it is also possible to specify two optional parameters in the user interface of SPMF:

If you are using the command line interface of SPMF, it is also possible to use these optional parameters by adding them at the end of the command. For example:
java -jar spmf.jar run FPGrowth_itemsets contextPasquier99.txt output.txt 40% 2 3
means to run the above example but to only find frequent itemsets containing at least 2 items and no more than 3 items.

Performance

There exists several algorithms for mining frequent itemsets. In SPMF, you can try for example Apriori, AprioriTID, Eclat, HMine, Relim and more. Among all these algorithms, FPGrowth is generally the fastest and most memory efficient algorithm. You can see a performance comparison by clicking on the "performance" section of this website.

Where can I get more information about the FPGrowth algorithm?

This is the journal article describing FPGrowth:

Jiawei Han, Jian Pei, Yiwen Yin, Runying Mao: Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach. Data Min. Knowl. Discov. 8(1): 53-87 (2004)

Also, for a good overview of frequent itemset mining algorithms, you may read this survey paper.