Mining constraint violations

ACM Transactions on Database Systems

Volumn 32, Issue 1, 2007, Pages

  Ceri, Stefano ^a   Di Giunta, Francesco ^a   Lanzi, Pier Luca ^a  

^a POLITECNICO DI MILANO   (Italy)

Author keywords

Deviation detection; Probabilistic models; Relational data mining

Indexed keywords

CONSTRAINT THEORY; DATA STRUCTURES; MATHEMATICAL MODELS; PROBABILISTIC LOGICS; RELATIONAL DATABASE SYSTEMS;

DEVIATION DETECTION; PROBABILISTIC MODELS; RELATIONAL DATA MINING;

DATA MINING;

EID: 33947632005     PISSN: 03625915     EISSN: 15574644     Source Type: Journal    
DOI: 10.1145/1206049.1206055     Document Type: Article

References (35)

1. AGRAWAL, R., IMIELINSKI, T., AND SWAMI, A. 1993. Mining association rules between sets of items in large databases. In Proceedings of the ACM SIGMOD International Conference on Management of Data (Washington, DC).
2. AGRAWAL, R. AND SRIKANT, R. 1994. Fast algorithms for mining association rules. In Proceedings of the 20th International Conference on Very Large Data Bases. Morgan Kaufmann, San Fransisco, CA. 487-499.
3. ARNING, A., AGRAWAL, R., AND RAGHAVAN, P. 1996. A linear method for deviation detection in large databases. In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining. 164-169.
4. AXELSSON, S. 2000. Intrusion detection systems: A survey and taxonomy. Tech. Rep. 99-15, Chalmers University of Technology, Göteborg, Sweden.
5. BOLTON, R. J. AND HAND, D. J. 2002. Statistical fraud detection: A review. Statis. Sci. 17, 3, 235-255.
6. CERI, S. AND WIDOM, J. 1990. Deriving production rules for constraint maintenance. In Proceedings of the 16th International Conference on Very Large Data Bases. Morgan Kaufmann, San Fransisco, CA. 566-577.
7. CHEN, H., CHUNG, W., XU, J. J., WANG, G., QIN, Y., AND CHAU, M. 2004. Crime data mining: A general framework and some examples. IEEE Computer 37, 4, 50-56.
8. COOK, D. J. AND HOLDER, L. B. 2000. Graph-Based data mining. IEEE Intell. Syst. 15, 2, 32-41.
9. CORREA, J. C. 2001. Interval estimation of the parameters of the multinomial distribution. Interstat, (Oct.).
10. DEHASPE, L. AND TOIVONEN, H. T. T. 2001. Discovery of relational association rules. In Relational Data Mining, S. Dzeroski and N. Lavrac, Eds. Springer Verlag.
11. DŽEROSKI, S. 2003. Multi-Relational data mining: An introduction. SIGKDD Explor. Newslett. 5, 1, 1-16.
12. GARCIA-MOLINA, H., ULLMAN, J. D., AND WIDOM, J. D. 2001. Database Systems: The Complete Book. Prentice Hall.
13. GETOOR, L. AND DIEHL, C. P. 2005. Link mining: A survey. SIGKDD Explor. 7, 2, 3-12.
14. GETOOR, L., FRIEDMAN, N., KOLLER, D., AND TASKAR, B. 2003. Learning probabilistic models of link structure. J. Mach. Learn. Res. 3, 679-707.
15. GOETHALS, B. AND DEN BUSSCHE, J. V. 2002. Relational association rules: Getting WARMBR. In Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery in Data Mining, D. J. Hand et al. Eds. Lecture Notes in Computer Science, vol. 2447. Springer Verlag. 125-139.
16. HITCHCOCK, C. R. Fall 2002. Probabilistic causation. In The Stanford Encyclopedia of Philosophy, E. N. Zalta, Ed. Fall.
17. ILYAS, I. F., MARKL, V., HAAS, P. J., BROWN, P., AND ABOULNAGA, A. 2004. Cords: Automatic discovery of correlations and soft functional dependencies. In Proceedings of the SIGMOD Conference, G. Weikum et al. Eds. 647-658.
18. KENDALL, M. G. AND STUART, A. 1963. The Advanced Theory of Statistics, 2nd Ed. vol. 1. Charles Griffin Ltd. London.
19. KING, R. S. AND LEGENDRE, J. J. 2003. Discovery of functional and approximate functional dependencies in relational databases. J. Appl. Math. Decision Sci. 7, 1, 49-59.
20. KIVINEN, J. AND MANNILA, H. 1995. Approximate inference of functional dependencies from relations. Theoret. Comput. Sci. 149, 1, 129-149.
21. LIBEN-NOWELL, D. AND KLEINBERG, J. 2003. The link prediction problem for social networks. In Proceedings of the 12th International Conference on Information and Knowledge Management (New Orleans, LA). 556-559.
22. SHOU-DE LIN AND CHALUPSKY, H. 2003. Unsupervised link discovery in multi-relational data via rarity analysis. In Proceedings of the 3rd IEEE International Conference on Data Mining (Melbourne, FL).
23. MOONEY, R. J., MELVILLE, P., TANG, L. R., SHAVINK, J., DE CASTRO DUTRA, I., PAGE, D., AND SANTOS COSTA, V. 2002. Relational data mining with inductive logic programming for link discovery. In Proceedings of the National Science Foundation Workshop on Next Generation Data Mining. Baltimore, MD, USA.
24. NEAPOLITAN, R. E. 2004. Learning Bayesian Networks. Prentice Hall, New York.
25. PEARL, J. 2000. Causality: Models, Reasoning, and Inference, Cambridge University Press.
26. POPESCUL, A. AND UNGAR, L. H. 2003. Statistical relational learning for link prediction. In Proceedings of the Workshop on Learning Statistical Models from Relational Data at IJCAI (Acapulco, Mexico).
27. QI, H. AND WANG, J. 2004. A model for mining outliers from complex data sets. In Proceedings of the ACM Symposium on Applied Computing. ACM Press, New York. 595-599.
28. SHAO, J. 1999. Mathematical Statistics. Springer Verlag.
29. SILVERSTEIN, C., BRIN, S., AND MOTWANI, R. 1998. Beyond market baskets: Generalizing association rules to dependence rules. Data Mining Knowl. Discov. 2, 1, 39-68.
30. SILVERSTEIN, C., BRIN, S., MOTWANI, R., AND ULLMAN, J. D. 2000. Scalable techniques for mining causal structures. Data Mining Knowl. Discov. 4, 2-3, 163-192.
31. SILVERSTEIN, C., MOTWANI, R., AND BRIN, S. 1997. Beyond market baskets: Generalizing association rules to correlations. In Proceedings of the ACM SIGMOD International Conference on Management of Data (Tucson, AZ).
32. TASKAR, B., WONG, M.-F, ABBEEL, P., AND KOLLER, D. 2004. Link prediction in relational data. In Advances in Neural Information Processing Systems 16, S. Thrun et al. Eds. MIT Press, Cambridge, MA.
33. ULLMAN, J. D. 1980. Principles of Database Systems. Computer Science Press, New York.
34. WIDOM, J. AND CERI, S. 1995. Active Database Systems: Triggers and Rules For Advanced Database Processing. Morgan Kaufmann, San Fransisco, CA.
35. ŻYTKOW, J. M. AND RAUCH, J., EDS. 1999. Proceedings of the Principles of Data Mining and Knowledge Discovery: 3rd European Conference (PKDD). Prague, Czech Republic. Springer Verlag.