Constraint Programming for Data Mining and Machine Learning

By Luc De Raedt, Tias Guns, Siegfried Nijssen. 2010

71 citations recorded by the source corpus.

Machine learning and data mining have become aware that using constraints when learning patterns and rules can be very useful. To this end, a large number of special purpose systems and techniques have been developed for solving such constraint-based mining and learning problems. These techniques have, so far, been developed independently of the general purpose tools and principles of constraint programming known within the field of artificial intelligence. This paper shows that off-the-shelf constraint programming techniques can be applied to various pattern mining and rule learning problems (cf. also (De Raedt, Guns, and Nijssen 2008; Nijssen, Guns, and De Raedt 2009)). This does not only lead to methodologies that are more general and flexible, but also provides new insights into the underlying mining problems that allow us to improve the state-of-the-art in data mining. Such a combination of constraint programming and data mining raises a number of interesting new questions and challenges.

Subfields: Computational Theory and Mathematics, Computer Networks and Communications, Information Systems

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Citations in the corpus

Learning Optimal Classification Trees Using a Binary Linear Program Formulation (2019, methodology): “This power has led to interesting new approaches for learning models and rules, see e.g., (Bessiere, Hebrard, and OSullivan 2009; De Raedt, Guns, and Nijssen 2010; Narodytska et al. 2018; Verwer, Zhang, and Ye 2017).”
Trusted Machine Learning for Probabilistic Models (2016, unknown): “Do not distribute. programming (Raedt et al., 2010).”