Anomaly detection through quasi-functional dependency analysis

Journal of Digital Information Management

Volumn 5, Issue 4, 2007, Pages 191-200

  Bruno, Giulia ^a   Garza, Paolo ^a   Quintarelli, Elisa ^b   Rossato, Rosalba ^b  

^a POLITECNICO DI TORINO   (Italy)

^b POLITECNICO DI MILANO   (Italy)

Author keywords

Anomaly detection; Association rules; Data mining; Qausi functional dependencies

Indexed keywords

ASSOCIATION RULES; DATABASE SYSTEMS; KNOWLEDGE BASED SYSTEMS; LEARNING SYSTEMS; LOGIC PROGRAMMING; XML;

ANOMALY DETECTION; QAUSI-FUNCTIONAL DEPENDENCIES;

DATA MINING;

EID: 44949112157     PISSN: 09727272     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (25)

1. Agrawal, R., Srikant, R (1994). Fast algorithms for mining assisciation rules in large data-bases. In: International Conference on Very Large Data Bases, 478-499. Morgan Kaufmann.
2. Aggarwal, C, C., Yu, P. S (2001). Outlier Detection for High Dimensional Data. Proceedings of SIGMOD Conference, 37-46.
3. Angiulli, F., Greco, G., Palopoli, L (2006), Outlier Detection by Logic Programming. ACM Transaction on Computational Logic. To appear
4. Angiulli, F., Pizzuti, C (2005). Outlier mining in large high dimensional datasets. IEEE Transactions on Knowledge and Data Engineering, 17 (2) 203-215.
5. Arenas, M., Libkin, L (2004), A Normal Form for XML Documents. ACM Transactions on Database Systems, 29 (1) 195-232.
6. Baralis, E., Garza, P., Quintarelli, E., Tanca, L. (2004), Answering Queries on XML Data by means of Association Rules. Current Trends in Database Technology, Vol. 3268. Springer-Verlag.
7. Apiletti, D., Bruno, G., Ficarra, E., Baralis, E. (2006), Data Cleaning and Semantic Improvement in Biological Databases. Journal of Integrative Bioinformatics., 3(2).
8. Breunig, M., Kriegel, H., Hg, R., Sander, J. (2000), LOF: Identifying density-based local outliers, In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, 93-104.
9. Ceri, S., Di Guinta, F., Lanzi, P. (to appear). Mining Constraint Violations. ACM-Transactions on Database Systems.
10. Elmasri, R., Navathe, S. B. (2005). Foundamentals of DATABASE SYSTEMS. Pearson, Addison Wesley.
11. NIST/SEMATECH e-Handbook of Statistical Methods, http://www.iti.nist.gov/ div898/handbook/, 2007.
12. Knorr, E., Hg, R., Tucakov, R (2000), Distance-Based Outlier :Algorithms and Applications, VLDB Journal. 8(34), 237-253.
13. Papadimitriou, S,, Kitagawa, H., Gibbons. P., Faloutsos, C (2003). LOCI: Fast outlier detection using the local correlation integral. Proceedings of 19th1, International Conference on Data Engineering (ICDE), 315-326.
14. Lay, M. The DBLP bibliography server, http://dblp.unitrier.de/xml.
15. The TPC benchmark H. Transaction Processing Performance Council, http:// wwwtpc.org/tpch/default.
16. World Wide Web Consortium (1998). Extensible Markup Language (XML) 1.0, httpl/www.w3C.org/TR/REC-xml/,
17. World Wide Web Consortium (2002). XQuery: An XML Query Language, http:// www.w3C.org/TR/REC-xml/.
18. Goethals, B., Zaki, M (2003). Advances in frequent itemset mining implementation: Report on FIMI'03. SIGKDD Explorations Newsletter 6 (1) 109-117.
19. Han, J., Kamber, M (2000). Data Mining: Concepts and Techniques. Morgan Kaufmann, 2000.
20. Yamanishi, K., Takeichi, J., Williams, G. (2000). On-Line Unsupervised Outlier Detection Using Finite Mixtures with Discounting Learning Algorithms. Proceedings of 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 320-324.
21. Scholkopf, B., Smola, A,J (2002). Learning with kernels. Cambridge, London, MIT.
22. Girolami, M (2001). Mercer Kernel Based Clustering in Feature Space. IEEE Transactions on Neural Networks, 13 (4) 780-784.
23. Breitenbach, M., Grudic, G Z. (20M. Clustering through ranking on manifolds, In: Proceedings of International Conference on Machine Learning (ICML 2005), 73-80.
24. Abe, N., Zadrozny, B., Langford, J. (2006). Outlier detection by active learning. Proceedings of International Conference on Knowledge Discovery and Data Minmg (KDD 2006), 504-509.
25. Newman, D.J. & Hettich, S. & Blake, C.L. & Merz, C.J. (1998). UCI Repository of machine learning databases [http://www.ics.uci.edu/~mlearn/MLRepository.html. Irvine, CA: University of California, Department of Information and Computer Science.