Exploiting incomparability in solution dominance : improving general purpose constraint-based mining
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In data mining, finding interesting patterns is a challenging task. Constraint-based mining is a well-known approach to this, and one for which constraint programming has been shown to be a well-suited and generic framework. Constraint dominance programming (CDP) has been proposed as an extension that can capture an even wider class of constraint-based mining problems, by allowing us to compare relations between patterns. In this paper we improve CDP with the ability to specify an incomparability condition. This allows us to overcome two major shortcomings of CDP: finding dominated solutions that must then be filtered out after search, and unnecessarily adding dominance blocking constraints between incomparable solutions. We demonstrate the efficacy of our approach by extending the problem specification language ESSENCE and implementing it in a solver-independent manner on top of the constraint modelling tool CONJURE. Our experiments on pattern mining tasks with both a CP solver and a SAT solver show that using the incomparability condition during search significantly improves the efficiency of dominance programming and reduces (and often eliminates entirely) the need for post-processing to filter dominated solutions.
Kocak , G , Akgun , O , Guns , T & Miguel , I J 2020 , Exploiting incomparability in solution dominance : improving general purpose constraint-based mining . in G De Giacomo , A Catala , B Dilkina , M Milano , S Barro , A Bugarín & J Lang (eds) , Frontiers in Artificial Intelligence and Applications : ECAI 2020, 24th European Conference on Artificial Intelligence, 29 August-8 September 2020, Santiago de Compostela, Spain . IOS Press , Amsterdam , pp. 331-338 , 24th European Conference on Artificial Intelligence (ECAI2020) , Santiago de Compostela , Spain , 29/08/20 . https://doi.org/10.3233/FAIA200110conference
Frontiers in Artificial Intelligence and Applications
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