SPMF documentation > Mining Frequent Itemsets using the Apriori Algorithm

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

How to run this example?

What is Apriori?

Apriori is an algorithm for discovering frequent itemsets in transaction databases. It was proposed by Agrawal & Srikant (1993).

What is the input of the Apriori algorithm?

The input 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 Apriori algorithm?

Apriori 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 Apriori is run on the previous transaction database with a minsup of 40 % (2 transactions), Apriori 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 for Apriori 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: 

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 four sequences in the SPMF format.

Then, if we apply a sequential pattern mining 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.


The Apriori algorithm is an important algorithm for historical reasons and also because it is a simple algorithm that is easy to learn. However, faster and more memory efficient algorithms have been proposed. If efficiency is required, it is recommended to use a more efficient algorithm like FPGrowth instead of Apriori. You can see a performance comparison of Apriori, FPGrowth, and other frequent itemset mining algorithms by clicking on the "performance" section of this website.

Implementation details

In SPMF, there is also an implementation of Apriori that uses a hash-tree as an internal structure to store candidates. This structure provide a more efficient way to count the support of itemsets. This version of Apriori is named "Apriori_with_hash_tree" in the GUI of SPMF and the command line. For the source code version, it can be run by executing the test file MainTestAprioriHT_saveToFile.java. This version of Apriori can be up to twice faster than the regular version in some cases but it uses more memory. This version of Apriori has two parameters: (1) minsup and (2) the number of child nodes that each node in the hash-tree should have. For the second parameter, we suggest to use the value 30.

Where can I get more information about the Apriori algorithm?

This is the technical report published in 1994 describing Apriori.

R. Agrawal and R. Srikant. Fast algorithms for mining association rules in large databases. Research Report RJ 9839, IBM Almaden Research Center, San Jose, California, June 1994.

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

You can also view a video presentation of the Apriori algorithm

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