Mining Frequent Sequential Patterns Using the CM-SPAM Algorithm (SPMF documentation)

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

How to run this example?

What is CMSPAM?

CM-SPAM (2013) is a sequential pattern mining algorithm based on the SPAM algorithm.

The main difference is that CM-SPAM utilizes a new technique named co-occurrence pruning to prune the search space, which makes it faster than the original SPAM algorithm.

What is the input of CM-SPAM?

The input of CM-SPAM is a sequence database and a user-specified threshold named minsup (a value in [0,1] representing a percentage). Moreover, the implementation in SPMF adds another parameter, which is the maximum sequential pattern length in terms of items.

A sequence database is a set of sequences where each sequence is a list of itemsets. An itemset is an unordered set of items. For example, the table shown below contains four sequences. The first sequence, named S1, contains 5 itemsets. It means that item 1 was followed by items 1 2 and 3 at the same time, which were followed by 1 and 3, followed by 4, and followed by 3 and 6. It is assumed that items in an itemset are sorted in lexicographical order. This database is provided in the file " contextPrefixSpan.txt" of the SPMF distribution. Note that it is assumed that no items appear twice in the same itemset and that items in an itemset are lexically ordered.

ID Sequences
S1 (1), (1 2 3), (1 3), (4), (3 6)
S2 (1 4), (3), (2 3), (1 5)
S3 (5 6), (1 2), (4 6), (3), (2)
S4 (5), (7), (1 6), (3), (2), (3)

What is the output of CM-SPAM?

CM-SPAM discovers all frequent sequential patterns occurring in a sequence database (subsequences that occurs in more than minsup sequences of the database.

To explain more formally what is a sequential pattern, it is necessary to review some definition.

A sequential pattern is a sequence. A sequence SA = X1, X2, ... Xk, where X1, X2... Xk are itemsets is said to occur in another sequence SB = Y1, Y2, ... Ym, where Y1, Y2... Ym are itemsets, if and only if there exists integers 1 <= i1 < i2... < ik <= m such that X1 ⊆ Yi1, X2 ⊆ Yi2, ... Xk ⊆ Yik.

The support of a sequential pattern is the number of sequences where the pattern occurs divided by the total number of sequences in the database.

A frequent sequential pattern is a sequential pattern having a support no less than the minsup parameter provided by the user.

For example, if we run CM-SPAM with minsup= 50 %, 53 sequential patterns will be found. The list is too long to be presented here. An example of pattern found is " (1,2),(6)" which appears in the first and the third sequences (it has therefore a support of 50%). This pattern has a length of 3 because it contains three items. Another pattern is " (4), (3), (2)" . It appears in the second and third sequence (it has thus a support of 50 %). It also has a length of 3 because it contains 3 items.

Optional parameters

The CM-SPAM implementation allows to specify additional optional parameter(s) :

These parameters are available in the GUI of SPMF and also in the example "" 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 parameters in the command line, it can be done as follows. Consider this example:
java -jar spmf.jar run CM-SPAM contextPrefixSpan.txt output.txt 0.5 2 6 1,3 1 true
This command means to apply CM-SPAM on the file " contextPrefixSpan.txt" and output the results to " output.txt" . Moreover, it specifies that the user wants to find patterns for minsup = 0.5, and patterns must have a minimum length of 2 items, a maximum length of 6 items, must contain items 1 and 3, and have no gap between itemsets. Moreover, sequence ids should be output for each pattern found.

Now, let's say that you want to run the algorithm again with the same parameters except that you don't want to use the "required items" parameter. You could do as follows:
java -jar spmf.jar run CM-SPAM contextPrefixSpan.txt output.txt 0.5 2 6 "" 1 true

Input file format

The input file format is defined as follows. It is a text file where each line represents a sequence from a sequence database. Each item from a sequence is a positive integer and items from the same itemset within a sequence are separated by single space. Note that it is assumed that items within a same itemset are sorted according to a total order and that no item can appear twice in the same itemset. The value " -1" indicates the end of an itemset. The value " -2" indicates the end of a sequence (it appears at the end of each line). For example, the input file " contextPrefixSpan.txt" contains the following four lines (four sequences).

1 -1 1 2 3 -1 1 3 -1 4 -1 3 6 -1 -2
1 4 -1 3 -1 2 3 -1 1 5 -1 -2
5 6 -1 1 2 -1 4 6 -1 3 -1 2 -1 -2
5 -1 7 -1 1 6 -1 3 -1 2 -1 3 -1 -2

The first line represents a sequence where the itemset {1} is followed by the itemset {1, 2, 3}, followed by the itemset {1, 3}, followed by the itemset {4}, followed by the itemset {3, 6}. The next lines follow the same format.

Note that it is also possible to use a text file containing a text (several sentences) if the text file has the " .text" extension, as an alternative to the default input format. If the algorithm is applied on a text file from the graphical interface or command line interface, the text file will be automatically converted to the SPMF format, by dividing the text into sentences separated by ".", "?" and "!", where each word is considered as an item. Note that when a text file is used as input of a data mining algorithm, the performance 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. Each line is a frequent sequential pattern. Each item from a sequential pattern is a positive integer and items from the same itemset within a sequence are separated by single spaces. The value " -1" indicates the end of an itemset. On each line, the sequential pattern is first indicated. Then, the keyword " #SUP: " appears followed by an integer indicating the support of the pattern as a number of sequences. For example, a few lines from the output file from the previous example are shown below:

2 3 -1 1 -1 #SUP: 2
6 -1 2 -1 #SUP: 2
6 -1 2 -1 3 -1 #SUP: 2

The first line indicates that the frequent sequential pattern consisting of the itemset {2, 3}, followed by the itemset {1} has a support of 2 sequences. The next lines follow the same format.

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 "contextPrefixSpan.txt". Here we have modified the file to give names to the items:

1 -1 1 2 3 -1 1 3 -1 4 -1 3 6 -1 -2
1 4 -1 3 -1 2 3 -1 1 5 -1 -2
5 6 -1 1 2 -1 4 6 -1 3 -1 2 -1 -2
5 -1 7 -1 1 6 -1 3 -1 2 -1 3 -1 -2

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". The 9th line indicates that the symbol "-1" must be replaced by "|". 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:

apple | #SUP: 4 
orange | #SUP: 4 
tomato | #SUP: 4 
apple | orange | #SUP: 4 

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.


CM-SPAM is faster than SPAM and one of the best sequential pattern mining algorithm in SPMF according to our experiment in the CM-SPAM paper (see Performance section of the website for more details).

Where can I get more information about CM-SPAM?

The CM-SPAM algorithm is described in this article:

Fournier-Viger, P., Gomariz, A., Campos, M., Thomas, R. (2014). Fast Vertical Mining of Sequential Patterns Using Co-occurrence Information. Proc. 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2014), Part 1, Springer, LNAI, 8443. pp. 40-52.

Besides, you may read this survey of sequential pattern mining, which gives an overview of sequential pattern mining algorithms.