Thorough analysis of log data with dependency rules: Practical solutions and theoretical challenges

Proceedings - 12th IEEE International Conference on Data Mining Workshops, ICDMW 2012

Volumn , Issue , 2012, Pages 579-586

  Hämäläinen, Wilhelmiina ^a  

^a UNIVERSITY OF EASTERN FINLAND   (Finland)

Author keywords

Dependency rule; Discretization; Hierarchical variable; Intrinsic dimensionality; Log data; Numerical variable; Preprocessing.

Indexed keywords

DEPENDENCY RULES; DISCRETIZATIONS; HIERARCHICAL VARIABLES; INTRINSIC DIMENSIONALITY; LOG DATA; NUMERICAL VARIABLES; PREPROCESSING;

ALGORITHMS;

DATA MINING;

EID: 84873160096     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDMW.2012.97     Document Type: Conference Paper

References (17)

1. W. Hämäläinen, "Efficient search for statistically significant dependency rules in binary data," Ph.D. dissertation, Department of Computer Science, University of Helsinki, 2010, Report A-2010-2.
2. R. Agrawal, T. Imielinski, and A. Swami, "Mining association rules between sets of items in large databases," in Proc. 1993 ACM SIGMOD Int. Conf. Management of Data. ACM Press, 1993, pp. 207-216.
3. G. Webb and S. Zhang, "K-optimal rule discovery," Data Mining and Knowledge Discovery, vol. 10, no. 1, pp. 39-79, 2005.
4. W. Hämäläinen, "Kingfisher: an efficient algorithm for searching for both positive and negative dependency rules with statistical significance measures," Knowledge and Information Systems, vol. 32, no. 2, pp. 383-414, 2012.
5. S. Nijssen, T. Guns, and L. D. Raedt, "Correlated itemset mining in ROC space: a constraint programming approach," in Proc. 15th ACM SIGKDD Conf. Knowledge Discovery and Data Mining (KDD'09). ACM Press, 2009, pp. 647-656.
6. J. Li, "On optimal rule discovery," IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 4, pp. 460-471, 2006.
7. R. Pensa, J. Besson, and J.-F. Boulicaut, "A methodology for biologically relevant pattern discovery from gene expression data," in Discovery Science, ser. LNCS, vol. 3245. Springer, 2004, pp. 230-241.
8. N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal, "Discovering frequent closed itemsets for association rules," in Proc. 7th Int. Conf. Database Theory (ICDT'99), ser. LNCS, vol. 1540. Springer-Verlag, 1999, pp. 398-416.
9. G. Webb, "MagnumOpus software," http://www.giwebb.com/index. html. Retrieved 10.2. 2009.
10. M. Birch, "The detection of partial association, i: The 2×2 case," Journal of the Royal Statistical Society. Series B (Methodological), vol. 26, no. 2, pp. 313-324, 1964.
11. W. Hämäläinen, "Kingfisher v1.2 (software for statistical dependency rule mining)," http://www.cs.uef.fi/~whamalai/ kingfisher.html Version 1.2 release date 8.8. 2012.
12. R. Srikant and R. Agrawal, "Mining generalized association rules," Future Generation Computer Systems, vol. 13, no. 2-3, pp. 161-180, 1997.
13. R. Rastogi and K. Shim, "Mining optimized support rules for numeric attributes," Information Systems, vol. 26, no. 6, pp. 425-444, 2001.
14. K. Wang, S. Hock, W. Tay, and B. Liu, "Interestingness-based interval merger for numeric association rules," in Proc. 4th Int. Conf. Knowledge Discovery and Data Mining. AAAI Press, 1998, pp. 121-127.
15. Y. Ke, J. Cheng, and W. Ng, "Correlated pattern mining in quantitative databases," ACM Transactions on Database Systems, vol. 33, no. 3, 2008.
16. H. Grosskreutz and S. Rüping, "On subgroup discovery in numerical domains," in Machine Learning and Knowledge Discovery in Databases, Proc. ECML/PKDD 2009, Part I, ser. LNCS, vol. 5781. Springer, 2009, pp. 210-226.
17. N. Lavrac and D. Gamberger, "Relevancy in constraintbased subgroup discovery," in Constraint-Based Mining and Inductive Databases, ser. LNCS, vol. 3848. Springer, 2004, pp. 243-266.