@InProceedings{PagliosaPaglNona:2016:UnAtVa,
               author = "Pagliosa, Lucas and Pagliosa, Paulo and Nonato, Luis Gustavo",
          affiliation = "ICMC-USP and FACOM-UFMS and ICMC-USP",
                title = "Understanding Attribute Variability in Multidimensional 
                         Projections",
            booktitle = "Proceedings...",
                 year = "2016",
               editor = "Aliaga, Daniel G. and Davis, Larry S. and Farias, Ricardo C. and 
                         Fernandes, Leandro A. F. and Gibson, Stuart J. and Giraldi, Gilson 
                         A. and Gois, Jo{\~a}o Paulo and Maciel, Anderson and Menotti, 
                         David and Miranda, Paulo A. V. and Musse, Soraia and Namikawa, 
                         Laercio and Pamplona, Mauricio and Papa, Jo{\~a}o Paulo and 
                         Santos, Jefersson dos and Schwartz, William Robson and Thomaz, 
                         Carlos E.",
         organization = "Conference on Graphics, Patterns and Images, 29. (SIBGRAPI)",
            publisher = "IEEE Computer Society´s Conference Publishing Services",
              address = "Los Alamitos",
             keywords = "attribute-based clustering, high-dimensional data visualization, 
                         interactive visual analysis.",
             abstract = "Multidimensional Projection techniques can help users to find 
                         patterns in multidimensional data. However, while the 
                         visualization literature is rich in techniques designed to improve 
                         the projection itself, only a handful of papers shed light into 
                         the attributes that contribute to cluster formation or the spread 
                         of projected data. In this paper, we present a web-based 
                         visualization tool that enriches multidimensional projection 
                         layout with statistical measures derived from inputted data. Given 
                         a set of regions to analyze, we used statistical measures, such as 
                         variance, to highlight relevant attributes that contribute to the 
                         points' similarities in each region. Experimental tests show that 
                         our technique can help identify important attributes and explain 
                         projected data.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos, SP, Brazil",
      conference-year = "4-7 Oct. 2016",
                  doi = "10.1109/SIBGRAPI.2016.048",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2016.048",
             language = "en",
                  ibi = "8JMKD3MGPAW/3M5A64P",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3M5A64P",
           targetfile = "PID4370197.pdf",
        urlaccessdate = "2024, May 03"
}