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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3933 LNCS, Issue , 2006, Pages 86-103

  Calders, Toon ^a   Goethals, Bart ^a  

^a UNIVERSITY OF ANTWERP   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTATIONAL COMPLEXITY; COMPUTER SCIENCE; DATABASE SYSTEMS; FUNCTION EVALUATION;

DATA MINING ALGORITHMS; EXPERIMENTAL EVALUATION; IE TECHNIQUE; INCLUSION-EXCLUSION ALGORITHM;

DATA MINING;

EID: 33745792786     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11733492_6     Document Type: Conference Paper

References (17)

1. R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In J.B. Bocca, M. Jarke, and C. Zaniolo, editors, Proceedings 20th International Conference on Very Large Data Bases, pages 487-499. Morgan Kaufmann, 1994.
2. C. Borgelt and R. Kruse. Induction of association rules: Apriori implementation. In W. Härdie and B. Rönz, editors, Proceedings of the 15th Conference on Computational Statistics, pages 395-400, http://fuzzy.cs.uni- magdeburg.de/~borgelt/software.html, 2002. Physica-Verlag.
3. J.-F. Boulicaut, A. Bykowski, and C. Rigotti. Approximation of frequency queries by means of free-sets. In Proc. PKDD Int. Conf. Principles of Data Mining and Knowledge Discovery, pages 75-85, 2000.
4. A. Bykowski and C. Rigotti. A condensed representation to find frequent patterns. In Proc. PODS Int. Conf. Principles of Database Systems, 2001.
5. T. Calders and B. Goethals. Minimal k-free representations of frequent sets. In Proc. PKDD Int. Conf. Principles of Data Mining and Knowledge Discovery, pages 71-82, 2002.
6. T. Calders and B. Goethals. Mining all non-derivable frequent itemsets. In Proc. PKDD Int. Conf. Principles of Data Mining and Knowledge Discovery, pages 74-85. Springer, 2002.
7. S. Jaroszewicz and D. A. Simivici. Support approximations using bonferroni-type inequalities. In Proc. PKDD Int. Conf. Principles of Data Mining and Knowledge Discovery, pages 212-224, 2002.
8. D.E. Knuth. Fundamental Algorithms. Addison-Wesley, Reading, Massachusetts, 1997.
9. M. Kryszkiewicz and M. Gajek. Why to apply generalized disjunction-free generators representation of frequent patterns? In Proc. International Syposium on Methodologies for Intelligent Systems, pages 382-392, 2002.
10. H. Mannila. Local and global methods in data mining: Basic techniques and open problems. In ICALP 2002, 29th International Colloquium on Automata, Languages, and Programming, 2002.
11. H. Mannila and H. Toivonen. Multiple uses of frequent sets and condensed representations. In Proc. KDD Int. Conf. Knowledge Discovery in Databases, 1996.
12. R. Meo. Theory of dependence values. ACM Trans. on Database Systems, 25(3):380-406, 2000.
13. A. Moore and M.S. Lee. Cached sufficient statistics for efficient machine learning with large datasets. Journal of Artificial Intelligence Research, 8:67-91, 1998.
14. D. Pavlov, H. Mannila, and P. Smyth. Beyond independence: Probabilistic models for query approximation on binary transaction data. IEEE Trans. on Knowledge and Data Engineering, 15(6):1409-1421, 2003.
15. A. Savinov. Mining dependence rules by finding largest support quota. In ACM Symposium on Applied Computing, pages 525-529, 2004.
16. Craig Silverstein, Sergey Brin, and Rajeev Motwani. Beyond market baskets: Generalizing association rules to dependence rules. Data Mining and Knowledge Discovery, 2(1):39-68, 1998.
17. Z. Zheng, R. Kohavi, and L. Mason. Real world performance of association rule algorithms. In Proc. KDD Int. Conf. Knowledge Discovery in Databases, pages 401-406. ACM Press, 2001.