@InProceedings{DiasMing:2018:EXHiPo,
               author = "Dias, F{\'a}bio Felix and Minghim, Rosane",
          affiliation = "Instituto de Ci{\^e}ncias Matem{\'a}ticas e de 
                         Comuta{\c{c}}{\~a}o (ICMC), University of S{\~a}o Paulo (USP) 
                         and Instituto de Ci{\^e}ncias Matem{\'a}ticas e de 
                         Comuta{\c{c}}{\~a}o (ICMC), University of S{\~a}o Paulo (USP)",
                title = "xHiPP: eXtended Hierarchical Point Placement Strategy",
            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 = "information visualization, multidimensional projection, multilevel 
                         projection.",
             abstract = "The complexity and size of data have created challenges to data 
                         analysis. Although point placement strategies have gained 
                         popularity in the last decade to yield a global view of 
                         multidimensional datasets, few attempts have been made to improve 
                         visual scalability and offer multilevel exploration in the context 
                         of multidimensional projections and point placement strategies. 
                         Such approaches can be helpful in improving the analysis 
                         capability both by organizing visual spaces and allowing 
                         meaningful partitions of larger datasets. In this paper, we extend 
                         the Hierarchy Point Placement (HiPP), a strategy for multi-level 
                         point placement, in order to enhance its analytical capabilities 
                         and flexibility to handle current trends in visual data science. 
                         We have provided several combinations of clustering methods and 
                         projection approaches to represent and visualize datasets; added a 
                         choice to invert the original processing order from 
                         cluster-projection to projection-cluster; proposed a better way to 
                         initialize the partitions, and added ways to summarize image, 
                         audio, text and general data groups. The tool's code is made 
                         available online. In this article, we present the new tool (named 
                         xHiPP) and provide validation through case studies with simpler 
                         and more complex datasets (text and audio) to illustrate that the 
                         capabilities afforded by the extensions have managed to provide 
                         analysts with the ability to quickly gain insight and adjust the 
                         processing pipeline to their needs.",
  conference-location = "Foz do Igua{\c{c}}u, PR, Brazil",
      conference-year = "Oct. 29 - Nov. 1, 2018",
             language = "en",
           targetfile = "xhipp-extended-hierarchical.pdf",
        urlaccessdate = "2020, Dec. 04"
}