Abstract
While typical data mining approaches find patterns/models from data stored in a single data table, relational data mining and inductive logic programming approaches (Džeroski & Lavrač, 2001; Lavrač & Džeroski, 1994a) find patterns/models from data stored in more complex data structures, such as graphs, multiple tables, etc., involving multiple relations. This chapter shows how to construct relational features and how to derive a covering table from such complex data structures.
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- 1.
This chapter is based on Flach and Lavrač (2003).
- 2.
Full Prolog allows one to construct aggregate terms by means of functors. While this facility is crucial to Prolog as a programming language (essentially this is the mechanism for building up data structures), we ignore this possibility because only a few ILP systems are actually able to use functions, and structured terms can be converted into sequences of predicates via a process calledflattening (Rouveirol, 1994).
- 3.
Variable Y isuniversally quantified if \(\forall \!y \in Y\).
- 4.
Variable X isexistentially quantified if \(\exists x \in X\).
- 5.
In the context of relational databases, aforeign key is a field in a relational table that matches a candidate key of another table. The foreign key can be used to cross-reference tables.
- 6.
Put differently, SQL takes the Cartesian product of the tables in the FROM clause, selects the tuples that meet the conditions in the WHERE clause, and projects on the attributes in the SELECT clause.
- 7.
The original attributes have depth 0. A new variable has depth i + 1, where i is the maximum depth of all old variables of the literal where the new variable is introduced.
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Fürnkranz, J., Gamberger, D., Lavrač, N. (2012). Relational Features. In: Foundations of Rule Learning. Cognitive Technologies. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75197-7_5
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