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@InProceedings{LorenaQuiLorCarCes:2019:NeInLi,
               author = "Lorena, Luiz Henrique Nogueira and Quiles, Marcos Gon{\c{c}}alves 
                         and Lorena, Luiz Antonio Nogueira and Carvalho, Andr{\'e} C. P. 
                         L. F. de and Cespedes, Juliana Garcia",
          affiliation = "{Universidade Federal de S{\~a}o Paulo (UNIFESP)} and 
                         {Universidade Federal de S{\~a}o Paulo (UNIFESP)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Universidade de 
                         S{\~a}o Paulo (USP)} and {Universidade Federal de S{\~a}o Paulo 
                         (UNIFESP)}",
                title = "Qualitative data clustering: a new Integer Linear Programming 
                         model",
                 year = "2019",
         organization = "The International Joint Conference on Neural Networks",
            publisher = "IEEE",
             abstract = "Qualitative data clustering is a fundamental data analysis task, 
                         with applications in many areas, like medicine, sociology, and 
                         economics. An appealing way to deal with this task is via Integer 
                         Linear Programming, as it avoids inappropriate inferences by the 
                         final user. This approach has two main advantages: the data are 
                         directly used, without the need of being converted to quantitative 
                         values, and the optimal number of clusters is automatically 
                         obtained by solving the optimization problem. However, it might 
                         create large and redundant models, which can limit the size of the 
                         problems it can be applied. Recently, models that are more compact 
                         and able to avoid some redundancy have been proposed in the 
                         literature. These models consume less memory and are faster to 
                         obtain the optimal solution set. In this study, a new model is 
                         introduced and compared with the state-of-the-art alternatives 
                         using datasets from different application domains. Empirical 
                         results show that the new model outperforms its predecessors, 
                         achieving the optimal solution set with lower computational time 
                         and memory consumption.",
  conference-location = "Budapest, Hungary",
      conference-year = "14-19 July",
                 isbn = "978-172811985-4",
             language = "en",
        urlaccessdate = "01 dez. 2020"
}


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