Integer Linear Programming for Pattern Set Mining; with an Application to Tiling
| Autor: | Lakhdar Loukil, Samir Loudni, Yahia Lebbah, Patrice Boizumault, Albrecht Zimmermann, Bruno Crémilleux, Abdelkader Ouali |
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| Přispěvatelé: | Laboratoire d'Informatique et Technologies de l'Information d'Oran (LITIO), Université d'Oran Al-Sanya, Equipe CODAG - Laboratoire GREYC - UMR6072, Groupe de Recherche en Informatique, Image et Instrumentation de Caen (GREYC), Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), Normandie Université (NU)-Normandie Université (NU)-Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), Normandie Université (NU) |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Theoretical computer science
Heuristic Computer science Branch and price 02 engineering and technology Automatic summarization Task (project management) [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] Constraint (information theory) Set (abstract data type) [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Cluster analysis Integer programming Computer Science::Databases |
| Zdroj: | Pacific-Asia Conference on Knowledge Discovery and Data Mining Pacific-Asia Conference on Knowledge Discovery and Data Mining, May 2017, Jeju, South Korea. ⟨10.1007/978-3-319-57529-2_23⟩ Advances in Knowledge Discovery and Data Mining ISBN: 9783319575285 PAKDD (2) |
| Popis: | International audience; Pattern set mining is an important part of a number of data mining tasks such as classification, clustering, database tiling, or pattern summarization. Efficiently mining pattern sets is a highly challenging task and most approaches use heuristic strategies. In this paper, we formulate the pattern set mining problem as an optimization task, ensuring that the produced solution is the best one from the entire search space. We propose a method based on integer linear programming (ILP) that is exhaustive, declarative and optimal. ILP solvers can exploit different constraint types to restrict the search space, and can use any pattern set measure (or combination thereof) as an objective function, allowing the user to focus on the optimal result. We illustrate and show the efficiency of our method by applying it to the tiling problem. |
| Databáze: | OpenAIRE |
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