@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 = "29 Oct.-1 Nov. 2018",
doi = "10.1109/SIBGRAPI.2018.00053",
url = "http://dx.doi.org/10.1109/SIBGRAPI.2018.00053",
language = "en",
ibi = "8JMKD3MGPAW/3RPBD6H",
url = "http://urlib.net/ibi/8JMKD3MGPAW/3RPBD6H",
targetfile = "xhipp-extended-hierarchical.pdf",
urlaccessdate = "2025, July 05"
}