Arules - A computational environment for mining association rules and frequent item sets

Journal of Statistical Software

Volumn 14, Issue , 2005, Pages

  Hahsler, Michael ^a   Grün, Bettina ^b   Hornik, Kurt ^a  

^a VIENNA UNIVERSITY OF ECONOMICS AND BUSINESS   (Austria)

^b VIENNA UNIVERSITY OF TECHNOLOGY   (Austria)

Author keywords

Association rules; Data mining; Frequent itemsets; R

Indexed keywords

EID: 27544480125     PISSN: 15487660     EISSN: 15487660     Source Type: Journal    
DOI: 10.18637/jss.v014.i15     Document Type: Article

References (26)

1. Agrawal R, Imielinski T, Swami A (1993). "Mining Association Rules Between Sets of Items in Large Databases." In "Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data," pp. 207-216. ACM Press. URL http://doi.acm.org/10.1145/170035.170072.
2. Agrawal R, Srikant R (1994). "Fast Algorithms for Mining Association Rules." In JB Bocca, M Jarke, C Zaniolo (eds.), "Proc. 20th Int. Conf. Very Large Data Bases, VLDB," pp. 487-499. Morgan Kaufmann.
3. Bates D, Maechler M (2005). Matrix: A Matrix Package for R. R package version 0.95-5.
4. Berry M JA, Linoff GS (1997). Data Mining Techniques for Marketing, Sales and Customer Support. Wiley Computer Publishing.
5. Blake CL, Merz CJ (1998). UCI Repository of Machine Learning Databases. University of California, Irvine, Dept. of Information and Computer Sciences. URL http://www.ics.uci.edu/~mlearn/MLRepository.html.
6. Borgelt C (2003). "Efficient Implementations of Apriori and Eclat." In "FIMI'03: Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations,".
7. Borgelt C (2004). Apriori - Finding Association Rules/Hyperedges with the Apriori Algorithm. Working Group Neural Networks and Fuzzy Systems, Otto-von-Guericke-University of Magdeburg, Universitätsplatz 2, D-39106 Magdeburg, Germany. URL http://fuzzy.cs.uni-magdeburg.de/~borgelt/apriori.html.
8. Borgelt C, Kruse R (2002). "Induction of Association Rules: Apriori Implementation." In "Proc. 15th Conf. on Computational Statistics (Compstat 2002, Berlin, Germany)," Physika Verlag, Heidelberg, Germany.
9. Brin S, Motwani R, Ullman JD, Tsur S (1997). "Dynamic Itemset Counting and Implication Rules for Market Basket Data." In "SIGMOD 1997, Proceedings ACM SIGMOD International Conference on Management of Data," pp. 255-264. Tucson, Arizona, USA.
10. DuMouchel W, Pregibon D (2001). "Empirical Bayes Screening for Multi-Item Associations." In F Provost, R Srikant (eds.), "Proceedings of the ACM SIGKDD Intentional Conference on Knowledge Discovery in Databases & Data Mining (KDD01)," pp. 67-76. ACM Press.
11. Fowler M (2004). UML Distilled: A Brief Guide to the Standard Object Modeling Language. Addison-Wesley Professional, third edition.
12. Goethals B, Zaki MJ (eds.) (2003). FIMI'03: Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations. Sun SITE Central Europe (CEUR).
13. Goethals B, Zaki MJ (2004). "Advances in Frequent Itemset Mining Implementations: Report on FIMI'03." SIGKDD Explorations, 6(1), 109-117.
14. Hahsler M, Hornik K, Reutterer T (2005). "Implications of Probabilistic Data Modeling for Rule Mining." Report 14, Department of Statistics and Mathematics, Wirschaftsuniversität Wien, Research Report Series, Augasse 2-6, 1090 Wien. URL http://epub.wu-wien.ac.at/dyn/openURL?id=oai:epub.wu-wien.ac.at:epub-wu-01_7f0.
15. Hastie T, Tibshirani R, Friedman J (2001). The Elements of Statistical Learning. Springer-Verlag.
16. Hipp J, Güntzer U, Nakhaeizadeh G (2000). "Algorithms for Association Rule Mining - A General Survey and Comparison." SIGKDD Explorations, 2(2), 1-58.
17. Omiecinski ER (2003). "Alternative Interest Measures for Mining Associations in Databases." IEEE Transactions on Knowledge and Data Engineering, 15(1), 57-69.
18. Pasquier N, Bastide Y, Taouil R, Lakhal L (1999). "Discovering Frequent Closed Itemsets for Association Rules." In "Proceeding of the 7th International Conference on Database Theory, Lecture Notes In Computer Science (LNCS 1540)," pp. 398-416. Springer-Verlag.
19. Piatetsky-Shapiro G (1991). "Discovery, Analysis, and Presentation of Strong Rules." In G Piatetsky-Shapiro, WJ Frawley (eds.), "Knowledge Discovery in Databases," AAAI/MIT Press, Cambridge, MA.
20. R Development Core Team (2005). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-proj ect.org/.
21. Silverstein C, Brin S, Motwani R (1998). "Beyond Market Baskets: Generalizing Association Rules to Dependence Rules." Data Mining and Knowledge Discovery, 2, 39-68.
22. Srikant R, Agrawal R (1996). "Mining Quantitative Association Rules in Large Relational Tables." In HV Jagadish, IS Mumick (eds.), "Int. Conf. on Management of Data, SIGMOD," pp. 1-12. ACM Press.
23. Strehl A, Ghosh J (2003). "Relationship-Based Clustering and Visualization for High-Dimensional Data Mining." INFORMS Journal on Computing, 15(2), 208-230.
24. Zaki MJ (2000). "Scalable Algorithms for Association Mining." IEEE Transactions on Knowledge and Data Engineering, 12(3), 372-390.
25. Zaki MJ (2004). "Mining Non-Redundant Association Rules." Data Mining and Knowledge Discovery, 9, 223-248.
26. Zaki MJ, Parthasarathy S, Ogihara M, Li W (1997). "New Algorithms for Fast Discovery of Association Rules." Technical Report 651, Computer Science Department, University of Rochester, Rochester, NY 14627.