@Article{LorenaQuilCarvLore:2018:PrTeCl,
               author = "Lorena, Luiz Henrique Nogueira and Quiles, Marcos Gon{\c{c}}alves 
                         and Carvalho, Andr{\'e} Carlos Ponce de Leon Ferreira de and 
                         Lorena, Luiz Antonio Nogueira",
          affiliation = "{Universidade Federal de S{\~a}o Paulo (UNIFESP)} and 
                         {Universidade Federal de S{\~a}o Paulo (UNIFESP)} and 
                         {Universidade de S{\~a}o Paulo (USP)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Preprocessing technique for cluster editing via integer linear 
                         programming",
              journal = "Lecture Notes in Computer Science",
                 year = "2018",
               volume = "10954",
                pages = "287--297",
                 note = "14th International Conference on Intelligent Computing, ICIC 2018; 
                         Wuhan; China; 15 - 18 August 2018",
             keywords = "Cluster Editing, Preprocessing technique, Unsupervised learning.",
             abstract = "This paper addresses the Cluster Editing problem. The objective of 
                         this problem is to transform a graph into a disjoint union of 
                         cliques using a minimum number of edge modifications. This problem 
                         has been considered in the context of bioinformatics, document 
                         clustering, image segmentation, consensus clustering, qualitative 
                         data clustering among others. Here, we focus on the Integer Linear 
                         Programming (ILP) formulation of this problem. The ILP creates 
                         models with a large number of constraints. This limits the size of 
                         the problems that can be optimally solved. In order to overcome 
                         this limitation, this paper proposes a novel preprocessing 
                         technique to construct a reduced model that feasibly maintains the 
                         optimal solution set. In comparison to the original model, the 
                         reduced model preserves the optimal solution and achieves 
                         considerable computational time speed-up in the experiments 
                         performed on different datasets.",
                  doi = "10.1007/978-3-319-95930-6_27",
                  url = "http://dx.doi.org/10.1007/978-3-319-95930-6_27",
                 issn = "0302-9743",
             language = "en",
           targetfile = "lorena_preprocessing.pdf",
        urlaccessdate = "27 abr. 2024"
}