Mining Frequent Closed Itemsets using the Charm / dCharm Algorithms (SPMF documentation)

This example explains how to run the Charm / dCharm algorithms using the SPMF open-source data mining library.

How to run this example?

What is Charm?

Charm is an algorithm for discovering frequent closed itemsets in a transaction database. It was proposed by Zaki (2002).

dCharm is a variation of the Charm algorithm that is implemented with diffsets rather than tidsets. It has the same output and input as Charm.

What is the input of the Charm / dCharm 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 Charm / dCharm algorithm?

Charm outputs frequent closed itemsets. To explain what is a frequent closed itemset, it is necessary to review a few definitions.

An itemset is a unordered set of distinct items. The support of an itemset is the number of transactions that contain the itemset. For example, the itemset {1, 3} has a support of 3 because it appears in three transactions (t1, t3, t5) from the previous transaction database.

A frequent itemset is an itemset that appears in at least minsup transactions from the transaction database. A frequent closed itemset is a frequent itemset that is not included in a proper superset having exactly the same support. The set of frequent closed itemsets is thus a subset of the set of frequent itemsets. Why is it interesting to discover frequent closed itemsets ? The reason is that the set of frequent closed itemsets is usually much smaller than the set of frequent itemsets and it can be shown that no information is lost by discovering only frequent closed itemsets (because all the frequent itemsets can be regenerated from the set of frequent closed itemsets - see Zaki (2002) for more details).

If we apply Charm on the previous transaction database with a minsup of 40 % (2 transactions), we get the following result:

frequent closed itemsets support
{3} 4
{1, 3} 3
{2, 5} 4
{2, 3, 5} 3
{1, 2, 3, 5} 2
If you compare this result with the output from a frequent itemset mining algorithm like Apriori, you would notice that only 5 closed itemsets are found by Charm instead of about 15 itemsets by Apriori, which shows that the set of frequent closed itemset can be much smaller than the set of frequent itemsets.

How should I interpret the results?

In the results, each frequent closed itemset is annotated with its support. 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. It is a closed itemset because it has no proper superset having exactly the same support.

Input file format

The input file format used by CHARM 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 closed 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, we show below the output file for this example. The second line indicates the frequent itemset consisting of the item 1 and 3, and it indicates that this itemset has a support of 4 transactions.

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


The Charm algorithm is an important algorithm because it is one of the first depth-first algorithm for mining frequent closed itemsets. In SPMF, Charm and DCI_Closed are the two most efficient algorithms for frequent closed itemset mining.

Optional parameter(s)

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

These parameter(s) are available in the GUI of SPMF and also in the example(s) "MainTestCharm..._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 Charm_bitset contextPasquier99.txt output.txt 40% true
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% transactions, and that transaction ids should be output for each pattern found.

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 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.

Where can I get more information about the Charm algorithm?

This article describes the Charm algorithm:

Mohammed Javeed Zaki, Ching-Jiu Hsiao: CHARM: An Efficient Algorithm for Closed Itemset Mining. SDM 2002.

Here is an article describing the dCharm 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.