Titlebook: Data Mining and Constraint Programming; Foundations of a Cro Christian Bessiere,Luc De Raedt,Dino Pedreschi Book 2016 Springer Internationa

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Dale S. Huff,Chrystalle Katte Carreonlly relevant in the last decade, as researchers are increasingly investigating how to identify the most suitable existing algorithm for solving a problem instead of developing new algorithms. This survey presents an overview of this work focusing on the contributions made in the area of combinatoria

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Eduardo D. Ruchelli,Dale S. Huff problems more efficient, as surveyed in the previous chapter. In this chapter, we take a look at a detailed case study that leverages transformations between problem representations to make portfolios more effective, followed by extensions to the state of the art that make algorithm selection more

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Eduardo D. Ruchelli,Dale S. Hufftwo approaches to adjust the level of consistency depending on the instance and on which part of the instance we propagate. The first approach, parameterized local consistency, uses as parameter the . of values, which is a feature computed by arc consistency algorithms during their execution. Parame

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Eduardo D. Ruchelli,Dale S. Huffage for modeling combinatorial (optimisation) problems. This language is augmented with a library of functions and predicates that help modeling data mining problems and facilities for interfacing with databases. We show how MiningZinc can be used to model constraint-based itemset mining problems, f

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Eduardo D. Ruchelli,Dale S. Huffch other. Many criteria for what constitutes a good clustering have been identified in the literature; furthermore, the use of additional constraints to find more useful clusterings has been proposed. In this chapter, it will be shown that most of these clustering tasks can be formalized using optim

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