The fact that data is scattered over many tables causes many problems in the practice of data mining. To deal with this problem, one either constructs a single table by propositionalisation, or uses a Multi-Relational Data Mining algorithm. In either case, one has to deal with the non-determinacy of one-to-many relationships. In propositionalisation, aggregate functions have already proven to be powerful tools to handle this non-determinacy. In this paper we show how aggregate functions can be incorporated in the dynamic construction of patterns of Multi-Relational Data Mining. © 2002 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Knobbe, A. J., Siebes, A., & Marseille, B. (2002). Involving aggregate functions in Multi-Relational search. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2431 LNAI, pp. 287–298). Springer Verlag. https://doi.org/10.1007/3-540-45681-3_24
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