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- Luc De Raedt, Tias Guns, Siegfried Nijssen
- KDD
- 2008

The relationship between constraint-based mining and constraint programming is explored by showing how the typical constraints used in pattern mining can be formulated for use in constraint programming environments. The resulting framework is surprisingly flexible and allows us to combine a wide range of mining constraints in different ways. We implement… (More)

- Tias Guns, Siegfried Nijssen, Luc De Raedt
- Artif. Intell.
- 2011

- Siegfried Nijssen, Tias Guns, Luc De Raedt
- KDD
- 2009

Correlated or discriminative pattern mining is concerned with finding the highest scoring patterns w.r.t. a correlation measure (such as information gain). By reinterpreting correlation measures in ROC space and formulating correlated itemset mining as a constraint programming problem, we obtain new theoretical insights with practical benefits. More… (More)

- Tias Guns, Siegfried Nijssen, Luc De Raedt
- IEEE Transactions on Knowledge and Data…
- 2013

We introduce the problem of k-pattern set mining, concerned with finding a set of k related patterns under constraints. This contrasts to regular pattern mining, where one searches for many individual patterns. The k-pattern set mining problem is a very general problem that can be instantiated to a wide variety of well-known mining tasks including… (More)

- Luc De Raedt, Tias Guns, Siegfried Nijssen
- AAAI
- 2010

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… (More)

- Benjamin Négrevergne, Tias Guns
- CPAIOR
- 2015

The goal of constraint-based sequence mining is to find sequences of symbols that are included in a large number of input sequences and that satisfy some constraints specified by the user. Many constraints have been proposed in the literature, but a general framework is still missing. We investigate the use of constraint programming as general framework for… (More)

- Hong Sun, Tias Guns, Ana Carolina Fierro, Lieven Thorrez, Siegfried Nijssen, Kathleen Marchal
- Nucleic acids research
- 2012

Computationally retrieving biologically relevant cis-regulatory modules (CRMs) is not straightforward. Because of the large number of candidates and the imperfection of the screening methods, many spurious CRMs are detected that are as high scoring as the biologically true ones. Using ChIP-information allows not only to reduce the regions in which the… (More)

- Tias Guns, Siegfried Nijssen, Luc De Raedt
- PAKDD
- 2011

The pattern mining community has shifted its attention from local pattern mining to pattern set mining. The task of pattern set mining is concerned with finding a set of patterns that satisfies a set of constraints and often also scores best w.r.t. an optimisation criteria. Furthermore, while in local pattern mining the constraints are imposed at the level… (More)

- Tias Guns, Siegfried Nijssen, Albrecht Zimmermann, Luc De Raedt
- 2011 IEEE 11th International Conference on Data…
- 2011

Recently, constraint programming has been proposed as a declarative framework for constraint-based pattern mining. In constraint programming, a problem is modelled in terms of constraints and search is done by a general solver. Similar to most pattern mining algorithms, these solvers typically employ exhaustive depth-first search, where constraints are used… (More)

- Benjamin Négrevergne, Anton Dries, Tias Guns, Siegfried Nijssen
- 2013 IEEE 13th International Conference on Data…
- 2013

Finding small sets of interesting patterns is an important challenge in pattern mining. In this paper, we argue that several well-known approaches that address this challenge are based on performing pair wise comparisons between patterns. Examples include finding closed patterns, free patterns, relevant subgroups and skyline patterns. Although progress has… (More)