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Detecting small sets of relevant patterns from a given dataset is a centralchallenge in data mining. The relevance of a pattern is based on user-providedcriteria; typically, all patterns that satisfy certain criteria are consideredrelevant. Rule-based languages like Answer Set Programming (ASP) seemwell-suited for specifying such criteria in a form of constraints. Althoughprogress has been made, on the one hand, on solving individual mining problemsand, on the other hand, developing generic mining systems, the existing methodseither focus on scalability or on generality. In this paper we make stepstowards combining local (frequency, size, cost) and global (various condensedrepresentations like maximal, closed, skyline) constraints in a generic andefficient way. We present a hybrid approach for itemset, sequence and graphmining which exploits dedicated highly optimized mining systems to detectfrequent patterns and then filters the results using declarative ASP. Tofurther demonstrate the generic nature of our hybrid framework we apply it to aproblem of approximately tiling a database. Experiments on real-world datasetsshow the effectiveness of the proposed method and computational gains foritemset, sequence and graph mining, as well as approximate tiling.  Under consideration in Theory and Practice of Logic Programming (TPLP).

Hybrid ASP-based Approach to Pattern Mining