Knowledge Discovery in Databases (KDD) is a complex interactive process. The promising theoretical framework of inductive databases considers this is essentially a querying process. It is enabled by a query language which can deal either with raw data or patterns which hold in the data. Mining patterns turns to be the so-called inductive query evaluation process for which constraint-based Data Mining techniques have to be designed. An inductive query specifies declara-tively the desired constraints and algorithms are used to compute the patterns satisfying the constraints in the data. We survey important results of this active research domain. This chapter emphasizes a real breakthrough for hard problems concerning local pattern mining under various constraints and it points out the current directions of research as well.
CITATION STYLE
Boulicaut, J.-F., & Jeudy, B. (2009). Constraint-based Data Mining. In Data Mining and Knowledge Discovery Handbook (pp. 339–354). Springer US. https://doi.org/10.1007/978-0-387-09823-4_17
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