2019/10/01 by Gökberk Koçak, Koçak, Gökberk, Özgür Akgün +5
Computer Science · #Artificial Intelligence (cs.AI) #Constraint Satisfaction and Optimization #Data Mining Algorithms and Applications #Databases (cs.DB) #FOS: Computer and information sciences #Rough Sets and Fuzzy Logic
paper · pdf · doi:10.48550/arxiv.1910.00505
openalex publication_date 2019/10/01 · openalex created_date 2019/10/10 · openalex updated_date 2026/07/28
Finding interesting patterns is a challenging task in data mining. 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. Dominance programming has been proposed as an extension that can capture an even wider class of constraint-based mining problems, by allowing to compare relations between patterns. In this paper, in addition to specifying a dominance relation, we introduce the ability to specify an incomparability condition. Using these two concepts we devise a generic framework that can do a batch-wise search that avoids checking incomparable solutions. We extend the ESSENCE language and underlying modelling pipeline to support this. We use generator itemset mining problem as a test case and give a declarative specification for that. We also present preliminary experimental results on this specific problem class with a CP solver backend to show that using the incomparability condition during search can improve the efficiency of dominance programming and reduces the need for post-processing to filter dominated solutions.