Abstract
Inductive databases (IDBs) contain both data and patterns. Here we consider IDBs where patterns are polynomial equations. We present a constraint-based approach to answering inductive queries in this domain. The approach is based on heuristic search through the space of polynomial equations and can use subsumption and evaluation constraints on polynomial equations. We evaluate this approach on standard regression problems. We finally consider IDBs containing patterns in the form of polynomial equations as well as molecular fragments, where the two are combined in order to derive QSAR (Quantitative Structure-Activity Relationships) models.
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References
Bayardo, R.: Constraints in data mining. SIGKDD Explorations 4(1) (2002)
De Raedt, L.: Data mining as constraint logic programming. In: Computational Logic: From Logic Programming into the Future, Springer, Berlin (2002)
Džeroski, S., Blockeel, H., Kompare, B., Kramer, S., Pfahringer, B., Van Laer, W.: Experiments in predicting biodegradability. In: Proc. Ninth International Conference on Inductive Logic Programming, pp. 80–91. Springer, Berlin (1999)
Džeroski, S., Todorovski, L.: Discovering dynamics: from inductive logic programming to machine discovery. J. Intelligent Information Systems 4, 89–108 (1995)
Garofalakis, M., Rastogi, R.: Scalable data mining with model constraints. SIGKDD Explorations 2(2), 39–48 (2000)
Howard, P.H., Boethling, R.S., Jarvis, W.F., Meylan, W.M., Michalenko, E.M.: Handbook of Environmental Degradation Rates. Lewis Publishers (1991)
Imielinski, T., Mannila, H.: A database perspective on knowledge discovery. Communications of the ACM 39(11), 58–64 (1996)
Kramer, S., De Raedt, L.: Feature construction with version spaces for biochemical applications. In: Proc. Eighteenth International Conference on Machine Learning, pp. 258–265. Morgan Kaufmann, San Francisco (2001)
Mannila, H., Toivonen, H.: Levelwise search and borders of theories in knowledge discovery. Data Mining and Knowledge Discovery 1(3), 241–258 (1997)
Witten, H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Mateo (1999)
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© 2004 Springer-Verlag Berlin Heidelberg
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Džeroski, S., Todorovski, L., Ljubič, P. (2004). Inductive Databases of Polynomial Equations. In: Kambayashi, Y., Mohania, M., Wöß, W. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2004. Lecture Notes in Computer Science, vol 3181. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30076-2_16
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DOI: https://doi.org/10.1007/978-3-540-30076-2_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22937-7
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