Data mining as constraint logic programming

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

Volumn 2408, Issue PART2, 2002, Pages 526-547

  De Raedt, Luc ^a  

^a UNIVERSITY OF FREIBURG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATION THEORY; COMPUTER CIRCUITS; DATA MINING; PROLOG (PROGRAMMING LANGUAGE); QUERY LANGUAGES; QUERY PROCESSING; SEMANTICS;

CONSTRAINT LOGIC PROGRAMMING; INDUCTIVE DATABASE; PROCEDURAL ASPECTS; QUERY PATTERNS;

LOGIC PROGRAMMING;

EID: 23044533452     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45632-5_20     Document Type: Article

References (30)

1. R. Agrawal, T. Imielinski, A. Swami. Mining association rules between sets of items in large databases. In Proceedings of ACM SIGMOD Conference on Management of Data, pp. 207-216, 1993.
2. E. Baralis, G. Psaila. Incremental Refinement of Mining Queries. In Mukesh K. Mohania, A. Min Tjoa (Eds.) Data Warehousing and Knowledge Discovery, First International Conference DaWaK '99 Proceedings. Lecture Notes in Computer Science, Vol. 1676, Springer Verlag, pp. 173-182, 1999.
3. Jean-Francois Boulicaut, Mika Klemettinen, Heikki Mannila: Querying Inductive Databases: A Case Study on the MINE RULE Operator. In Proceedings of PKDD-98, Lecture Notes in Computer Science, Vol. 1510, Springer Verlag, pp. 194-202, 1998.
4. I. Bratko. Prolog Programming for Artificial Intelligence. Addison-Wesley, 1990. 2nd Edition.
5. W. Cohen, Whirl: a word-based information representation language. Artificial Intelligence, Vol. 118 (1-2), pp. 163-196, 2000.
6. L. Dehaspe, H. Toivonen and R.D. King. Finding frequent substructures in chemical compounds, in Proceedings of KDD-98, AAAI Press, pp. 30-36, 1998.
7. L. Dehaspe, H. Toivonen. Discovery of Frequent Datalog Patterns, in Data Mining and Knowledge Discovery Journal, Vol. 3 (1), pp. 7-36, 1999.
8. L. De Raedt, An inductive logic programming query language for database mining (Extended Abstract), in Proceedings of Artificial Intel ligence and Symbolic Computation, Lecture Notes in Artificial Intelligence, Vol. 1476, Springer Verlag, pp. 1-13, 1998.
9. L. De Raedt, A Logical Database Mining Query Language. In Proceedings of the 10th Inductive Logic Programming Conference, Lecture Notes in Artificial Intelligence, Vol. 1866, Springer Verlag, pp. 78-92, 2000.
10. L. De Raedt, S. Kramer, The level-wise version space algorithm and its application to molecular fragment finding, in Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence, Morgan Kaufmann, pp. 853-862, 2001.
11. R. Elmasri, S. Navathe. Fundamentals of database systems. Benjamin Cummings. 1994.
12. Usama M. Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth, Ramasamy Uthurusamy (Eds.). Advances in Knowledge Discovery, The MIT Press, 1996.
13. F. Giannotti, G. Manco: Querying Inductive Databases via Logic-Based User-Defined Aggregates. In Proceedings of PKDD 99, Lecture Notes in Artificial Intelligence, Vol. 1704, Springer Verlag, pp. 125-135, 1999.
14. J. Han, Y. Fu, K. Koperski, W. Wang, and O. Zaiane, DMQL: A Data Mining Query Language for Relational Databases, in SIGMOD'96 Workshop on Research Issues on Data Mining and Knowledge Discovery, Montreal, Canada, June 1996.
15. J. Han, L. V. S. Lakshmanan, and R. T. Ng, Constraint-Based, Multidimensional Data Mining, Computer, Vol. 32(8), pp. 46-50, 1999.
16. H. Hirsh. Generalizing Version Spaces. Machine Learning, Vol. 17(1), pp. 5-46 (1994).
17. T. Imielinski and H. Mannila. A database perspectivce on knowledge discovery. Communications of the ACM, Vol. 39(11), pp. 58-64, 1996.
18. T. Imielinski, A. Virmani, and A. Abdulghani. Application programming interface and query language for database mining. In Proceedings of KDD 96. AAAI Press, pp. 256-262, 1996.
19. Robert A. Kowalski. Algorithm = Logic + Control. Communications of the ACM, 22(7), pp. 424-436, 1979.
20. S. Kramer, L. De Raedt. Feature Construction with Version Spaces for Biochemical Applications, in Proceedings of the Eighteenth International Conference on Machine Learning, Morgan Kaufmann, 2001.
21. S. Kramer, L. De Raedt, C. Helma. Molecular Feature Mining in HIV Data, in Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM Press, pp. 136-143, 2001.
22. D. Gunopulos, H. Mannila, S. Saluja: Discovering All Most Specific Sentences by Randomized Algorithms. In Foto N. Afrati, Phokion Kolaitis (Eds.): Database Theory - ICDT '97, 6th International Conference, Lecture Notes in Computer Science, Vol. 1186, Springer Verlag, pp. 41-55, 1997.
23. H. Mannila and H. Toivonen, Levelwise search and borders of theories in knowledge discovery, Data Mining and Knowledge Discovery, Vol. 1(3), pp. 241-258, 1997.
24. H. Mannila. Inductive databases. In Proceedings of the International Logic Programming Symposium, The MIT Press, pp. 21-30, 1997.
25. Marriott, K. and Stuckey, P. J. Programming with constraints: an introduction. The MIT Press. 1998.
26. R. Meo, G. Psaila and S. Ceri, An extension to SQL for mining association rules. Data Mining and Knowledge Discovery, Vol. 2 (2), pp. 195-224, 1998.
27. C. Mellish. The description identification algorithm. Artificial Intelligence, Vol. 52 (2), pp, 151-168, 1990.
28. T. Mitchell. Generalization as Search, Artificial Intelligence, Vol. 18 (2), pp. 203-226, 1980.
29. G. Sablon, L. De Raedt, and Maurice Bruynooghe. Iterative Versionspaces. Artificial Intelligence, Vol. 69(1-2), pp. 393-409, 1994.
30. A. Inokuchi, T. Washio, H. Motoda. An Apriori-based algorithm for mining frequent substructures from graph data. In D. Zighed, J. Komorowski, and J. Zyktow (Eds.) Proceedings of PKDD 2000, Lecture Notes in Artificial Intelligence, Vol. 1910, Springer-Verlag, pp. 13-23, 2000.