I was doing some research on how to mine patterns from sequence data and I found a really good open sourced platform called SPMF.

SPMF is an open-source data mining platform written in Java. It is distributed under the GPL v3 license.

The link to the website:  http://www.philippe-fournier-viger.com/spmf/index.php

It offers implementations of 52 data mining algorithms for:

• sequential pattern mining,
• association rule mining,
• frequent itemset mining,
• sequential rule mining,
• clustering

It can be used as a standalone program with a user interface or from the command line. Moreover, the source code of each algorithm can be integrated in other Java software.

The following picture is a map which you can visualize the relationship between the various data mining algorithms offered in SPMF.

 

Supporting Algorithms

Sequential Pattern Mining Algorithms

• the PrefixSpan algorithm for mining frequent sequential patterns from a sequence database (Pei et al., 2004).
• the SPAM algorithm for mining frequent sequential patterns from a sequence database (Ayres, 2002)
• the BIDE+ algorithm for mining frequent closed sequential patterns from a sequence database (Wang et al. 2007)
• the SeqDIM algorithm for mining frequent multidimensional sequential patterns from a multi-dimensional sequence database (Pinto et al. 2001)
• the Songram et al. algorithm for mining frequent closed multidimensional sequential patterns from a multi-dimensional sequence database (Songram et al. 2006)
• the Fournier-Viger et al. algorithm, a sequential pattern mining algorithm that combines several features from well-known sequential pattern mining algorithms and also proposes some original features (Fournier-Viger et al., 2008):
  • mining sequences with minimum support by database-projection (based on PrefixSpan, Pei et al., 2004)
  • mining sequences with min/max time interval between events and min/max time length of a sequence (based on Hirate-Yamana, 2006)
  • mining closed sequences (based on the BIDE+ algorithm by Wang et al. 2007)
  • mining multi-dimensional sequences (based on Pinto et al. 2001)
  • mining closed multi-dimensional sequences (based on Songram et al. 2006 and Pasquier et al., 1999)
  • mining sequences with items having integer values and performing automatic clustering of these values (original extension described in Fournier-Viger et al., 2008)

Sequential Rule Mining Algorithms

• the RuleGrowth algorithm for mining sequential rules from a sequence database (Fournier-Viger et al., 2011, powerpoint)
• the TRuleGrowth algorithm for mining sequential rules from a sequence database with the window size constraint (Fournier-Viger, 2012a)
• the CMRules algorithm for mining sequential rules from a sequence database (Fournier-Viger et al., 2010, powerpoint)
• the CMDeo algorithm for mining sequential rules from a sequence database(Fournier-Viger et al., 2010)
• the TopSeqRules algorithm for mining the top-k sequential rules from a sequence database (Fournier-Viger et al., 2011, powerpoint)
• the RuleGen algorithm for mining sequential rules from a sequence database (Zaki et al, 2001)
• the TNS algorithm for mining top-k non-redundant sequential rules (Fournier-Viger 2012c) 

(more…)

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