Abstract
In many data mining tools that support regression tasks, training data are stored in a single table containing both the target field (dependent variable) and the attributes (independent variables). Generally, only intra-tuple relationships between the attributes and the target field are found, while inter-tuple relationships are not considered and (inter-table) relationships between several tuples of distinct tables are not even explorable. Disregarding inter-table relationships can be a severe limitation in many real-word applications that involve the prediction of numerical values from data that are naturally organized in a relational model involving several tables (multi-relational model). In this paper, we present a new data mining algorithm, named Mr-SMOTI, which induces model trees from a multi-relational model. A model tree is a tree-structured prediction model whose leaves are associated with multiple linear regression models. The particular feature of Mr-SMOTI is that internal nodes of the induced model tree can be of two types: regression nodes, which add a variable to some multiple linear models according to a stepwise strategy, and split nodes, which perform tests on attributes or the join condition and eventually partition the training set. The induced model tree is a multi-relational pattern that can be represented by means of selection graphs, which can be translated into SQL, or equivalently into first order logic expressions.
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Apice, A., Ceci, M., Malerba, D. (2003). Mining Model Trees: A Multi-relational Approach. In: Horváth, T., Yamamoto, A. (eds) Inductive Logic Programming. ILP 2003. Lecture Notes in Computer Science(), vol 2835. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39917-9_3
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DOI: https://doi.org/10.1007/978-3-540-39917-9_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20144-1
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