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Algorithms

SPMF contains implementations of 29 data mining algorithms.

Sequential Pattern Mining Algorithms

• the PrefixSpan algorithm, an algorithm for mining frequent sequential patterns from a sequence database (Pei et al., 2004).
• the BIDE+ algorithm, an 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)

Frequent Itemset Mining Algorithms

• the Apriori algorithm for discovering frequent itemsets from a binary context. (Agrawal & Srikant, 1994)
• the AprioriTID algorithm for discovering frequent itemsets from a binary context. (Agrawal & Srikant, 1994)
• the FP-Growth algorithm for discovering frequent itemsets from a binary context. (Han et al., 2004)
• the Eclat algorithm for discovering frequent itemsets from a binary context. (Zaki, 2000)
• the Relim algorithm for discovering frequent itemsets from a binary context (Borgelt, 2005)
• the Charm algorithm for discovering frequent closed itemsets from a binary context (Zaki and Hsiao, 2002)
• the AprioriClose algorithm for discovering frequent itemsets and frequent closed itemsets from a binary context (Pasquier et al., 1999)
• the Charm-MFI algorithm for discovering frequent closed itemsets and maximal frequent itemsets from a binary context (Szathmary et al. 2006)
• the AprioriInverse algorithm for mining all perfectly rare itemsets from a binary context (Koh & Roundtree, 2005)
• the AprioriRare algorithm for mining minimal rare itemsets and frequent itemsets from a binary context (Szathmary et al. 2007b)
• the Zart algorithm for discovering frequent closed itemsets and their minimal generators from a binary context (Szathmary et al. 2007)
• an algorithm for mining pseudo-closed itemsets from a binary context (Pasquier et al., 1999)
• the CloStream algorithm for mining frequent closed itemsets from a data stream (Yen et al, 2009)

Association Rule Mining Algorithms

• an algorithm for mining all association rules from a binary context (Agrawal & Srikant, 1994)
• an algorithm for mining the IGB informative and generic basis of association rules from a binary context (Gasmi et al., 2005)
• an algorithm for mining perfectly sporadic association rules (Koh & Roundtree, 2005)
• an algorithm for mining the Guigues-Duquenne basis for exact association rules from a binary context (Pasquier et al., 1999)
• an algorithm for mining the proper basis for approximative association rules from a binary context (Pasquier et al., 1999)
• an algorithm for mining the structural basis for approximative association rules from a binary context (Pasquier et al., 1999)
• an algorithm for mining closed association rules (Szathmary et al. 2006).
• an algorithm for mining minimal non redundant association rules (Kryszkiewicz, 1998)

Clustering Algorithms

• the K-Means algorithm for clustering integer values (MacQueen, 1967)
• an extended K-Means algorithm
• a hierarchical clustering algorithm