@Article{MoraisQuilSant:2014:IcGeDa,
               author = "Morais, Alessandra Marli M. and Quiles, M. G. and Santos, Rafael 
                         Duarte Coelho dos",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Universidade Federal de S{\~a}o Paulo (UNIFESP)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "Icon and geometric data visualization with a self-organizing map 
                         grid",
              journal = "Lecture Notes in Computer Science",
                 year = "2014",
               volume = "8584",
               number = "6",
                pages = "562--575",
             keywords = "Conformal mapping, Flow visualization, Geometry, Visualization, 
                         Dimensionality reduction, Geometric techniques, Intermediate 
                         complexity, Knowledge discovery in database, Kohonen 
                         self-organizing maps, Multiple dimensions, Topology preservation, 
                         Visualization technique, Data visualization.",
             abstract = "Data Visualization is an important tool for tasks related to 
                         Knowledge Discovery in Databases (KDD). Often the data to be 
                         visualized is complex, have multiple dimensions or features and 
                         consists of many individual data points, making visualization with 
                         traditional icon- and pixel-based and geometric techniques 
                         difficult. In this paper we propose a combination of icon-based 
                         and geometric-based visualization techniques backed up by a 
                         Self-Organizing Map, which allows dimensionality reduction and 
                         topology preservation. The technique is applied to some datasets 
                         of simple and intermediate complexity, and the results shows that 
                         it is possible to reduce clutter and facilitate identification of 
                         associations, clusters and outliers. © 2014 Springer International 
                         Publishing.",
                  doi = "10.1007/978-3-319-09153-2_42",
                  url = "http://dx.doi.org/10.1007/978-3-319-09153-2_42",
                 isbn = "9783319091525",
                 issn = "0302-9743",
                label = "scopus 2014-11 MoraisQuilSant:2014:IcGeDa",
             language = "en",
        urlaccessdate = "04 maio 2024"
}