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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)
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)
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)
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)
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)
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)
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)
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)