%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2016/07.20.18.35
%2 sid.inpe.br/sibgrapi/2016/07.20.18.35.08
%@doi 10.1109/SIBGRAPI.2016.048
%T Understanding Attribute Variability in Multidimensional Projections
%D 2016
%A Pagliosa, Lucas,
%A Pagliosa, Paulo,
%A Nonato, Luis Gustavo,
%@affiliation ICMC-USP
%@affiliation FACOM-UFMS
%@affiliation ICMC-USP
%E Aliaga, Daniel G.,
%E Davis, Larry S.,
%E Farias, Ricardo C.,
%E Fernandes, Leandro A. F.,
%E Gibson, Stuart J.,
%E Giraldi, Gilson A.,
%E Gois, João Paulo,
%E Maciel, Anderson,
%E Menotti, David,
%E Miranda, Paulo A. V.,
%E Musse, Soraia,
%E Namikawa, Laercio,
%E Pamplona, Mauricio,
%E Papa, João Paulo,
%E Santos, Jefersson dos,
%E Schwartz, William Robson,
%E Thomaz, Carlos E.,
%B Conference on Graphics, Patterns and Images, 29 (SIBGRAPI)
%C São José dos Campos, SP, Brazil
%8 4-7 Oct. 2016
%I IEEE Computer Society´s Conference Publishing Services
%J Los Alamitos
%S Proceedings
%K attribute-based clustering, high-dimensional data visualization, interactive visual analysis.
%X 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.
%@language en
%3 PID4370197.pdf