Mining High-Utility Patterns in Uncertain Tensors
| Autor: | Nicolas Nadisic, Loïc Cerf, Aurélien Coussat |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Mathematical optimization
Computer science Context (language use) 02 engineering and technology Constraint (information theory) Matrix (mathematics) Monotone polygon 020204 information systems 0202 electrical engineering electronic engineering information engineering Piecewise General Earth and Planetary Sciences 020201 artificial intelligence & image processing Tensor General Environmental Science Real number |
| Zdroj: | KES |
| ISSN: | 1877-0509 |
| DOI: | 10.1016/j.procs.2018.07.274 |
| Popis: | Transactional datasets are 0/1 matrices, which generically stand for objects having Boolean properties. If every cell of the matrix is additionally associated with a real number called utility, a high-utility itemset relates to a all-ones sub-matrix with utilities that sum to a high-enough value. This article shows that “having a total utility exceeding a threshold” is a piecewise (anti-)monotone constraint, even in presence of both positive and negative utilities. For that reason, a generic algorithm, multidupehack, can prune the search of the high-utility patterns defined in a broader context than the 0/1 matrix: the uncertain tensor. Moreover, the utilities may relate to only some of the dimensions, the patterns can be forced (or not) to be closed and to satisfy additional constraints. A real-world application, which exploits all those possibilities, is presented. Despite its versatility, the proposal is also shown competitive when it comes to mining 0/1 matrices, a special case treated by dozens of specific algorithms. |
| Databáze: | OpenAIRE |
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