Mining Frequent Itemsets using the Eclat / dEclat Algorithms (SPMF documentation)

This example explains how to run the Eclat / dEclat Algorithms using the SPMF open-source data mining library.

How to run this example?

What is Eclat ?

Eclat is an algorithm for discovering frequent itemsets in a transaction database. It was proposed by Zaki (2001). Contrarily to algorithms such as Apriori, Eclat uses a depth-first search for discovering frequent itemsets instead of a breath-first search.

dEclat is a variation of the Eclat algorithm that is implemented using a structure called "diffsets" rather than "tidsets".

What is the input of the Eclat 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 Eclat algorithm?

Eclat 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 Eclat is run on the previous transaction database with a minsup of 40 % (2 transactions), Eclat 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?

Each frequent 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 ECLAT 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.


There exists several algorithms for mining frequent itemsets. Eclat is one of the best. But generally, FPGrowth is a better algorithm. You can see a performance comparison by clicking on the "performance" section of this website. Note that recently (SPMF v0.96e), the Eclat implementation was optimized and is sometimes faster than FPGrowth.

Nevertheless, the Eclat algorithm is interesting because it uses a depth-first search. For some extensions of the problem of itemset mining such as mining high utility itemsets (see the HUI-Miner algorithm), the search procedure of Eclat works very well.

Implementation details

In SPMF, there are four versions of ECLAT. The first one is named "Eclat" and uses HashSets of Integers for representing sets of transaction IDs (tidsets). The second version is named "Eclat_bitset" and uses bitsets for representing tidsets. Using bitsets has the advantage of generally being more memory efficient and can also make the algorithm faster depending on the dataset.

There is also two versions of dEclat, which utilizes a structure called diffsets instead of tidsets. The versions having diffsets implemented as HashSets of integers and the version having diffsets implemented as bitsets are respectively named "dEclat_bitset" and "dEclat"

Optional parameter(s)

This implementation allows to specify additional optional parameter(s) :

These parameter(s) are available in the GUI of SPMF and also in the example(s) "MainTestEclat_..._saveToFile .java" provided in the source code of SPMF.

The parameter(s) can be also used in the command line with the Jar file. If you want to use these optional parameter(s) in the command line, it can be done as follows. Consider this example:

java -jar spmf.jar run Eclat contextPasquier99.txt output.txt 40% true 2
This command means to apply the algorithm on the file "contextPasquier99.txt" and output the results to "output.txt". Moreover, it specifies that the user wants to find patterns for minsup = 40%, and that transaction ids should be output for each pattern found. Also, it indicates that only frequent itemsets having no more than 2 items should be output.

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.

Where can I get more information about the Eclat algorithm?

Here is an article describing the Eclat algorithm:

Mohammed Javeed Zaki: Scalable Algorithms for Association Mining. IEEE Trans. Knowl. Data Eng. 12(3): 372-390 (2000)

Here is an article describing the dEclat variation:

Zaki, M.J., Gouda, K.: Fast vertical mining using diffsets. Technical Report 01-1, Computer Science Dept., Rensselaer Polytechnic Institute (March 2001) 10

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