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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)