Expressive power of an algebra for data mining

ACM Transactions on Database Systems

Volumn 31, Issue 4, 2006, Pages 1169-1214

  Calders, Toon ^a   Lakshmanan, Laks V S ^b   Ng, Raymond T ^b   Paredaens, Jan ^c  

^a EINDHOVEN UNIVERSITY OF TECHNOLOGY   (Netherlands)

^b UNIVERSITY OF BRITISH COLUMBIA   (Canada)

^c UNIVERSITY OF ANTWERP   (Belgium)

Author keywords

Algebra; Data mining; Expressive power

Indexed keywords

DATA MINING ALGEBRA; GENERIC OPERATORS; MINING LOOP;

ALGEBRA; ALGORITHMS; DATA MINING; INFORMATION TECHNOLOGY; MATHEMATICAL MODELS; RELATIONAL DATABASE SYSTEMS;

DATABASE SYSTEMS;

EID: 33846219163     PISSN: 03625915     EISSN: 15574644     Source Type: Journal    
DOI: 10.1145/1189769.1189770     Document Type: Conference Paper

References (34)

1. ABITEBOUL, S. AND HILLEBRAND, G. G. 1995. Space usage in functional query languages. In Proceedings of the ICDT International Conference on Database Theory, G. Gottlob and M. Vardi, Eds. Lecture Notes in Computer Science, vol. 893. Springer-Verlag, London, U.K., 439-454.
2. ABITEBOUL, S., HULL, R., AND VIANU, V. 1995. Foundations of Databases. Addison-Wesley, Reading, MA.
3. AGRAWAL, A. AND SRIKANT, R. 1994. Fast algorithms for mining association rules. In Proceedings of the VLDB International Conference on Very Large Data Bases. Morgan Kaufmann, San Francisco, CA, 487-499.
4. 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. ACM Press, New York, NY, 207-216.
5. BOULICAUT, J.-F., KLEMETTINEN, M., AND MANNILA, H. 1999. Modeling KDD processes within the inductive database framework. In Proceedings of the DaWaK International Conference on Data Warehousing and Knowledge Discovery. Lecture Notes in Computer Science, vol. 1676. Springer-Verlag, London, U.K., 293-302.
6. BREIMAN, L., FRIEDMAN, J., OLSHEN, R., AND STONE, C. 1984. Classification and Regression Trees. Wadsworth, Belmont, CA.
7. CALDERS, T., RIGOTTI, C., AND BOULICAUT, J.-F. 2006. A survey on condensed representations for frequent sets. In Constraint-Based Mining and Inductive Databases, J.-F. Boulicaut, L. de Raedt, and H. Mannila, Eds. Lecture Notes in Computer Science, vol. 3848. Springer-Verlag, London, U.K.
8. CHAUDHURI, S., NARASAYYA, V. R., AND SARAWAGI, S. 2002. Efficient evaluation of queries with mining predicates. In Proceedings of ICDEproc. IEEE Computer Society, Press, Los Alamitos, CA, 529-540.
9. CONSENS, P. AND MENDELZON, A. 1993. Low complexity aggregation in graphlog and datalog. Theoret. Comput. Sci. 116, 1& 2, 95-116.
10. DANTZIG, G. 1963. Linear Programming and Extensions. Princeton University Press, Princeton, NJ.
11. GAREY, M. AND JOHNSON, D. S. 1979. Computer and Intractability: A Guide to NP-Completeness. W. H. Freeman, New York, NY.
12. GEIST, I. AND SATTLER, K. 2002. Towards data mining operators in database systems: Algebra and implementation. In Proceedings of the DBFusion International Workshop on Databases, Documents, and Information Fusion. Vol. 124. CEUR-WS, Karlsruhe, Germany.
13. GUTTMAN, A. 1984. R-trees: A dynamic index structure for spatial searching. In Proceedings of the ACM SIGMOD International Conference on Management of Data. ACM Press, New York, NY, 47-57.
14. HAN, J., FU, Y., WANG, W., KOPERSKI, K., AND ZAIANE, O. 1996. DMQL: A data mining query language for relational databases. In Proceedings of the ACM SIGMOD International Conference on Management of Data. ACM Press, New York, NY, 27-33.
15. HAN, J., PEI, J., AND YIN, Y. 2000. Mining frequent patterns without candidate generation. In Proceedings of the ACM SIGMOD International Conference on Management of Data, W. Chen, J. Naughton, and P. Bernstein, Eds. ACM Press, New York, 1-12.
16. HAND, D., MANNILA, H., AND SMYTH, P. 2001. Principles of Data Mining. MIT Press, Cambridge, MA.
17. IMIELINSKI, T. AND MANNILA, H. 1996. A database perspective on knowledge discovery. Comm. ACM 39, 11, 58-64.
18. IMIELINSKI, T. AND VIRMANI, A. 1999. MSQL: A query language for database mining. Knowl. Disc. Data Min. 3, 4, 373-408.
19. JOHNSON, T., LAKSHMANAN, L. V., AND NG, R. 2000. The 3W model and algebra for unified data mining. In Proceedings of the VLDB International Conference on Very Large Data Bases. Morgan Kaufmann, San Francisco, CA, 21-32.
20. KENNEDY, K. AND MCKINLEY, K. S. 1994. Maximizing loop parallelism and improving data locality via loop fusion and distribution. In Proceedings of the 6th International Workshop on Languages and Compilers for Parallel Computing. Springer-Verlag, London, U.K., 301-320.
21. LAW, Y.-N., WANG, H., AND ZANIOLO, C. 2004. Query languages and data models for database sequences and data streams. In Proceedings of the VLDB International Conference on Very Large Data Bases. Morgan Kaufmann, San Francisco, CA, 492-503.
22. LIBKIN, L. AND WONG, L. 1997. On the power of aggregation in relational query languages. In Proceedings of the DBPL Workshop on Databases and Programming Languages. Lecture Notes in Computer Science, vol. 1369. Springer-Verlag, London, U.K., 260-280.
23. MANNILA, H. AND TOIVONEN, H. 1996. Multiple uses of frequent sets and condensed representations. In Proceedings of the KDD International Conference on Knowledge Discovery in Databases. ACM Press, New York, NY.
24. MEO, R., PSAILA, G., AND CERI, S. 1996. A new SQL-like operator for mining association rules. In Proceedings of the VLDB International Conference on Very Large Data Bases. Morgan Kaufmann, San Francisco, CA, 122-133.
25. MURTY, K. G. 1983. Linear Programming. John Wiley & Sons, New York, NY.
26. NETZ, A., CHAUDHURI, S., FAYYAD, U. M., AND BERNHARDT, J. 2001. Integrating data mining with sql databases: Ole DB for data mining. In Proceedings of the IEEE ICDE International Conference on Data Engineering. IEEE Computer Society Press, Los Alamitos, CA, 379-387.
27. PARADAENS, J., VAN DEN BUSSCHE, J., AND VAN GUCHT, D. 1994. Towards a theory of spatial database queries. In Proceedings of the PODS International Conference on Principles of Database Systems. ACM Press, New York, NY, 279-288.
28. PARADAENS, J. AND VAN GUCHT, D. 1998. Possibilities and limitations of using flat operators in nested algebra expressions. In Proceedings of the PODS International Conference on Principles of Database Systems. ACM Press, New York, NY, 29-38.
29. SARAWAGI, S., THOMAS, S., AND AGRAWAL, R. 1998. Integrating association rule mining with relational database systems: Alternatives and implications. In Proceedings of the ACM SIGMOD International Conference on Management of Data. ACM Press, New York, NY, 343-354.
30. TSUR, D., ULLMAN, J. D., ABITEBOUL, S., CLIFTON, C., MOTWANI, R., NESTOROV, S., AND ROSENTHAL, A. 1998. Query flocks: A generalization of association-rule mining. In Proceedings of the ACM SIGMOD International Conference on Management of Data. ACM Press, New York, NY, 1-12.
31. VAN DEN BUSSCHE, J. 2001. Simulation of the nested relational algebra by the flat relational algebra, with an application to the complexity of evaluating powerset algebra expressions. Theoret. Comput. Sci. 254, 1-2, 363-377.
32. WANG, H. AND ZANIOLO, C. 2000. Nonmonotonic reasoning in LDL++. In Logic-Based Artificial Intelligence, J. Minker, Ed. Kluwer, Dordrecht, The Netherlands, 523-544.
33. WANG, H. AND ZANIOLO, C. 2003. ATLaS: A native extension of sql for data mining. In Proceedings of the Third SIAM International Conference on Data Mining. SIAM Press, Philadelphia, PA.
34. ZANIOLO, C. 2005. Mining databases and data streams with query languages and rules. In Proceedings of the ECML-PKDD 2005 Workshop on Knowledge Discovery in Inductive Databases. Lecture Notes in Computer Science, vol. 3933. Springer-Verlag, London, U.K.