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Accepted paper at ICDM 2021 Workshop OEDM 2021

SCORER-Gap: Sequentially Correlated Rules for Event Recommendation Considering Gap Size



Ludwig Zellner, Janina Sontheim, Florian Richter, Gabriel Lindner, Thomas Seidl


The Workshop on Optimization Based Techniques for Emerging Data Mining Problems (OEDM 2021)
in conjunction with the 21st IEEE International Conference on Data Mining (ICDM 2021),
07–10 December 2021, Auckland, New Zealand


In our paper we aim at the discovery of partially-ordered sequential rules which satisfy a given correlation gap constraint. Applying this constraint to the support threshold determines a more relevant rule, among other parameters. We also require it in sparse datasets, where long sequences with many distinct events exist. By focusing on the gap size between antecedent and consequent of a rule, we show that the resulting vast number of rules gets highly reduced while keeping the flexibility between a minimum and a maximum distance in between.