Data Mining and Constraint Programming: Foundations of a by Christian Bessiere, Luc De Raedt, Lars Kotthoff, Siegfried

By Christian Bessiere, Luc De Raedt, Lars Kotthoff, Siegfried Nijssen, Barry O'Sullivan, Dino Pedreschi

A winning integration of constraint programming and information mining has the capability to guide to a brand new ICT paradigm with a ways achieving implications. it may possibly switch the face of information mining and laptop studying, in addition to constraint programming expertise. it is going to not just permit one to exploit info mining recommendations in constraint programming to spot and replace constraints and optimization standards, but in addition to hire constraints and standards in information mining and computing device studying so one can realize versions suitable with previous wisdom.

This e-book stories on a few key effects bought in this built-in and move- disciplinary process in the ecu FP7 FET Open undertaking no. 284715 on “Inductive Constraint Programming” and a couple of linked workshops and Dagstuhl seminars. The e-book is established in 5 elements: historical past; studying to version; studying to resolve; constraint programming for information mining; and showcases.

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D. thesis, Universit´e de France-Compt´e (1995) 25. : Exploiting runtime variation in complete solvers. In: Handbook of Satisfiability, pp. 271–288 (2009) 26. : Heavy-tailed phenomena in satisfiability and constraint satisfaction problems. J. Autom. Reason. 24(1–2), 67–100 (2000) 27. : Boosting combinatorial search through randomization. In: Proceedings of the 15th National Conference on Artificial Intelligence, AAAI 1998, pp. 431–437 (1998) 28. : Increasing tree search efficiency for constraint satisfaction problems.

In particular, [VSKSvdH09] enables the user to define a desired classifier performance. The work provides a complete analysis when a classifier is constrained to a desired level of precision (defined as F-measure and/or to tp-/fp-rate related performance measures). The learned model is adjusted to achieve the desired performance, abstaining to classifying ambiguous examples in order to guarantee the required level of performance. Furthermore, [VSKSvdH09] studies the effect on an ROC curve when ambiguous instances are left unclassified.

In the context of Las Vegas algorithms [5] it is proven to be universally optimal, achieving a runtime that is only a logarithmic factor from an optimal restart strategy where the runtime distribution of the underlying algorithm is fully known, and no other universal strategy can do better by more than a constant factor [37]. Alternatively, the geometric [55] sequence increases the cutoff by a constant factor between each run. Restarting is typically combined with randomisation in the variable and value heuristics to avoid repeatedly exploring the same search space.

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