Integer Linear Programming for Pattern Set Mining; with an Application to Tiling

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10235)


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.


Integer Linear Program Local Pattern Pattern Mining Integer Linear Program Model Redundancy Constraint 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Laboratory of LITIOUniversity of Oran 1OranAlgeria
  2. 2.Normandie Univ, UNICAEN, ENSICAEN, CNRS, GREYCCaenFrance

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