SPMF: A java open-source pattern mining library

Journal of Machine Learning Research

Volumn 15, Issue , 2015, Pages 3389-3393

  Fournier Viger, Philippe ^a   Gomariz, Antonio ^b   Gueniche, Ted ^c   Soltani, Azadeh ^c   Wu, Cheng Wei ^d   Tseng, Vincent S ^d  

^a UNIVERSITÉ DE MONCTON   (Canada)

^b UNIVERSITY OF MURCIA   (Spain)

^c FERDOWSI UNIVERSITY OF MASHHAD   (Iran)

^d NATIONAL CHENG KUNG UNIVERSITY   (Taiwan)

Author keywords

Data mining; Frequent pattern mining; Library; Open source; Sequence database; Transaction database

Indexed keywords

ALGORITHMS; DATABASE SYSTEMS; JAVA PROGRAMMING LANGUAGE; LIBRARIES; OPEN SOURCE SOFTWARE;

COMMAND LINE INTERFACE; DATA MINING ALGORITHM; FREQUENT PATTERN MINING; GNU GENERAL PUBLIC LICENSE; OPEN-SOURCE; PERFORMANCE COMPARISON; SEQUENCE DATABASE; TRANSACTION DATABASE;

DATA MINING;

EID: 84919772030     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article

References (19)

1. R. Agrawal and R. Srikant. Fast algorithms for mining association rules in large databases. In Proceedings of the Twentieth International Conference on Very Large Data Bases, pages 487-499, Santiago, Chile, 1994.
2. R. Agrawal and R. Srikant. Mining sequential patterns. In Proceedings of the Eleventh International Conference on Data Engineering, pages 3-14, Taipei, Taiwan, 1995.
3. J. Ayres, J. Gehrke, T, Yiu, J. Flannick. Sequential pattern mining using bitmaps. In Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 429-435, Edmonton, Alberta, Canada, July 2002.
4. R. Bayardo, B. Goethals, M. J. Zaki, Proceedings of the IEEE International Conference on Data Mining Workshop on Frequent Itemset Mining Implementations Brighton, UK, 2004.
5. Coron. Software available at: http://coron.loria.fr/site/index.php
6. P. Fournier-Viger, R. Nkambou and V.S. Tseng. RuleGrowth: mining sequential rules com-mon to several sequences by pattern-growth. In Proceedings of the 26th Symposium on Applied Computing., ACM Press, pages 954-959, Taitung, Taiwan, 2011.
7. J. Han, J. Pei, Y. Yin, R. Mao. Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Mining and Know. Discovery, 8(1):53-87, 2004.
8. Galicia. Software available at: http://www.iro.umontreal.ca/∼galicia/
9. A. Gomariz, M. Campos, R. Marin, B. Goethals. ClaSP: An efficient algorithm for mining frequent closed sequences. Proceedings of the 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, pages 50-61, Gold Coast, Australia, 2013.
10. Illimine. Software available at: http://illimine.cs.uiuc.edu/
11. Knime. Software available at: http://www.knime.org/
12. M. Liu, J.-F. Qu. Mining high utility itemsets without candidate generation. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pages 55-64, Maui, HI, USA, 2012
13. LUCS-KDD. Software available at: http://cgi.csc.liv.ac.uk/∼frans/KDD/Software/
14. Mahout. Software available at: http://mahout.apache.org/
15. J. Pei, J. Han et al. Mining sequential patterns by pattern-growth: the prefixspan approach. IEEE Transactions on Knowledge and Data Engineering, 16(10):1-17, 2004.
16. R. Srikant, R. Agrawal. Mining sequential patterns: generalizations and performance improvements. Proceedings of the 5th International Conference on Extending Database Tech-nology, pages 3-17, Avignon, France, 1996.
17. J. Wang, J. Han, C. Li. Frequent closed sequence mining without candidate maintenance. IEEE Transactions on Knowledge and Data Engineering, 19(8):1042-1056, 2007.
18. I. H. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, 2005.
19. M. J. Zaki. Scalable algorithms for association mining. IEEE Transactions on Knowledge and Data Engineering, 12(3):372-390.