author = "Montero, Ad{\'a}n Echemend{\'{\i}}a and Falc{\~a}o, Alexandre 
          affiliation = "Laboratory of Image Data Science, Institute of Computing, 
                         University of Campinas and Laboratory of Image Data Science, 
                         Institute of Computing, University of Campinas",
                title = "A Divide-and-Conquer Clustering Approach based on Optimum-Path 
            booktitle = "Proceedings...",
                 year = "2018",
               editor = "Ross, Arun and Gastal, Eduardo S. L. and Jorge, Joaquim A. and 
                         Queiroz, Ricardo L. de and Minetto, Rodrigo and Sarkar, Sudeep and 
                         Papa, Jo{\~a}o Paulo and Oliveira, Manuel M. and Arbel{\'a}ez, 
                         Pablo and Mery, Domingo and Oliveira, Maria Cristina Ferreira de 
                         and Spina, Thiago Vallin and Mendes, Caroline Mazetto and Costa, 
                         Henrique S{\'e}rgio Gutierrez and Mejail, Marta Estela and Geus, 
                         Klaus de and Scheer, Sergio",
         organization = "Conference on Graphics, Patterns and Images, 31. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "clustering, optimum-path forest, image segmentation, image 
                         foresting transform, divide-and-conquer.",
             abstract = "Data clustering is one of the main challenges when solving Data 
                         Science problems. Despite its progress over almost one century of 
                         research, clustering algorithms still fail in identifying groups 
                         naturally related to the semantics of the problem. Moreover, the 
                         technological advances add crucial challenges with a considerable 
                         data increase, which are not handled by most techniques. We 
                         address these issues by proposing a divide-and-conquer approach to 
                         a clustering technique, which is unique in finding one group per 
                         dome of the probability density function of the data --- the 
                         Optimum-Path Forest (OPF) clustering algorithm. Our approach can 
                         use all samples, or at least many samples, in the unsupervised 
                         learning process without affecting the grouping performance and, 
                         therefore, being less likely to lose relevant grouping 
                         information. We show that it can obtain satisfactory results when 
                         segmenting natural images into superpixels.",
  conference-location = "Foz do Igua{\c{c}}u, PR, Brazil",
      conference-year = "Oct. 29 - Nov. 1, 2018",
             language = "en",
           targetfile = "34.pdf",
        urlaccessdate = "2020, Dec. 02"