@InProceedings{ArteroOliv:2004:EfExVi,
               author = "Artero, Almir Olivette and Oliveira, Maria Cristina Ferreira",
          affiliation = "{Instituto de Ci{\^e}ncias Matem{\'a}ticas e 
                         Computa{\c{c}}{\~a}o} and {Universidade de S{\~a}o Paulo} and 
                         Caixa Postal 668, 13.560-970, S{\~a}o Carlos SP, Brasil",
                title = "Viz3D: Effective exploratory visualization of large 
                         multidimensional data sets.",
            booktitle = "Proceedings...",
                 year = "2004",
               editor = "Ara{\'u}jo, Arnaldo de Albuquerque and Comba, Jo{\~a}o Luiz Dihl 
                         and Navazo, Isabel and Sousa, Ant{\^o}nio Augusto de",
         organization = "Brazilian Symposium on Computer Graphics and Image Processing, 17. 
                         (SIBGRAPI) - Ibero-American Symposium on Computer Graphics, 2 
                         (SIACG)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "information visualization, visual data mining, clustering.",
             abstract = "We propose a multidimensional visualization technique, named 
                         Viz3D, that creates a 3D representation of n-dimensional data that 
                         may be interactively manipulated by users to handle visual 
                         cluttering and object occlusion. The projection performed in Viz3D 
                         is comparable in quality with the 3D projections obtained with 
                         well-known dimensionality reduction techniques, at a lower 
                         complexity cost. While a 3D projection conveys more information, 
                         giving the user more control of the visual representation and an 
                         additional dimension, as compared to 2D, visual cluttering and 
                         object occlusion are still a problem in handling large 
                         multidimensional data sets. To produce more effective 
                         visualizations, two strategies are introduced. Dimensionality is 
                         handled with a similarity clustering of attributes prior to 
                         projection. Data set size is handled with a new strategy of 
                         visualizing data densities, rather than individual data records. 
                         Both the direct and density Viz3D visualizations provide the basis 
                         for a user driven visual clustering approach applicable to high 
                         dimensional data sets that is very simple, intuitive and 
                         effective.",
  conference-location = "Curitiba, PR, Brazil",
      conference-year = "17-20 Oct. 2004",
                  doi = "10.1109/SIBGRA.2004.1352979",
                  url = "http://dx.doi.org/10.1109/SIBGRA.2004.1352979",
             language = "en",
                  ibi = "6qtX3pFwXQZeBBx/De5Q4",
                  url = "http://urlib.net/ibi/6qtX3pFwXQZeBBx/De5Q4",
           targetfile = "4418_Artero_Oliveira.pdf",
        urlaccessdate = "2024, Apr. 28"
}