Abstract
Inductive databases (IDBs) contain both data and patterns. Inductive Queries (IQs) are used to access, generate and manipulate the patterns in the IDB. IQs are conjunctions of primitive constraints that have to be satisfied by target patterns: they can be different for different types of patterns. Constraint-based data mining algorithms are used to answer IQs.
So far, mostly the problem of mining frequent patterns has been considered in the framework of IDBs: the types of patterns considered include frequent itemsets, episodes, Datalog queries, sequences, and molecular fragments. Here we consider the problem of constraint-based mining for predictive models, where the data mining task is regression and the models are polynomial equations.
More specifically, we first define the pattern domain of polynomial equations. We then present a complete and a heuristic solver for this domain. We evaluate the use of the heuristic solver on standard regression problems and illustrate its use on a toy problem of reconstructing a biochemical reaction network. Finally, we consider the use of a combination of different pattern domains (molecular fragments and polynomial equations) for practical applications in modeling quantitative structure-activity relationships (QSARs).
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Džeroski, S., Todorovski, L., Ljubič, P. (2006). Inductive Queries on Polynomial Equations. In: Boulicaut, JF., De Raedt, L., Mannila, H. (eds) Constraint-Based Mining and Inductive Databases. Lecture Notes in Computer Science(), vol 3848. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11615576_7
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DOI: https://doi.org/10.1007/11615576_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31331-1
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