Abstract
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.
Original language | English |
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Title of host publication | AAAI-10 / IAAI-10 - Proceedings of the 24th AAAI Conference on Artificial Intelligence and the 22nd Innovative Applications of Artificial Intelligence Conference |
Pages | 1671-1675 |
Number of pages | 5 |
Volume | 3 |
Publication status | Published - 1 Nov 2010 |
Event | 24th AAAI Conference on Artificial Intelligence and the 22nd Innovative Applications of Artificial Intelligence Conference, AAAI-10 / IAAI-10 - Atlanta, GA, United States Duration: 11 Jul 2010 → 15 Jul 2010 |
Conference
Conference | 24th AAAI Conference on Artificial Intelligence and the 22nd Innovative Applications of Artificial Intelligence Conference, AAAI-10 / IAAI-10 |
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Country/Territory | United States |
City | Atlanta, GA |
Period | 11/07/10 → 15/07/10 |