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- W3180472016 abstract "In this thesis, we study objective interesting pattern mining processes on datasets such as used in itemset mining. We focus on the notions of objectivity and meaningfulness in mining processes. We establish a link between the meaningfulness of a mining process and that of its corresponding mathematical modeling. We formulate a number of recommendations in terms of modeling choices for increasing both meaningfulness and objectivity. We also establish a link between the study of objective interesting pattern mining and the issue of the automation of scientific discovery.Our theoretical analysis exhibits the adequacy of considering maximum entropy models in such mining processes. We then proceed with presenting a novel mathematical construction for such models, based on a notion of constrained independence, which is specifically adapted to the itemset context. Based on this construction and on tools from algebraic geometry, we present an exact method for computing maximum entropy models. Last, based on our recommendations for the mathematical modeling of pattern mining processes and our notion of constrained independence, we present a new objective interesting pattern mining algorithm." @default.
- W3180472016 created "2021-07-19" @default.
- W3180472016 creator A5026148727 @default.
- W3180472016 date "2021-05-21" @default.
- W3180472016 modified "2023-09-25" @default.
- W3180472016 title "Meaningful objective frequency-based interesting pattern mining" @default.
- W3180472016 hasPublicationYear "2021" @default.
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