SPMF documentation > Mining Progressive Sequential Patterns Using the ProSecCo Algorithm

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

How to run this example?

To run the implementation of ProSecCo :

What is ProSecCo?

ProSecCo is an algorithm for discovering progressive frequent sequential patterns in sequence databases, proposed by (Servan-Schreiber et al. (2001).

What is the input of ProSecCo?

The input of ProSecCo is a sequence database and four parameters:

For a description of the parameters, please see the research paper describing the ProSecCo algorithm.

A sequence database is a set of sequences where each sequence is a list of itemsets. An itemset is an unordered set of distinct 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)

 

In this example, we will assume that the parameters are set as:

What is the output of ProSecCo?

ProSecCo discovers all progressive frequent sequential patterns occurring in a sequence database.

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:

1 -14 -1 #SUP: 2
1 -16 -1 #SUP: 2
2 3 -11 -1 #SUP: 2

The third line indicates that the frequent sequential pattern consisting of the itemset {2, 3}, followed by the itemset {1} has a support of 2 sequences.

Performance

This is the original implementation of ProSecCo

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:

@CONVERTED_FROM_TEXT
@ITEM=1=apple
@ITEM=2=orange
@ITEM=3=tomato
@ITEM=4=milk
@ITEM=5=bread
@ITEM=6=noodle
@ITEM=7=rice
@ITEM=-1=|
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.

Implementation details

Note that in the source code, we also provide examples of how to keep the result into memory instead of saving it to a file. This can be useful if the algorithms are integrated into another Java software. The file MainTestProsecco_saveToMemory.java show how to run the algorithm to keep the result into memory.

Where can I get more information about ProSecCo?

The ProSecCo algorithm is described in this article:

Servan-Schreiber, S., Riondato, M., Zgraggen, E. (2018). ProSecCo: Progressive Sequence Mining with Convergence Guarantees. Proceedings of IEEE ICDM 2018.

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

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