Identity statement area
Reference TypeConference Proceedings
Last Update2018: administrator
Metadata Last Update2020: administrator
Citation KeyDiasMing:2018:EXHiPo
TitlexHiPP: eXtended Hierarchical Point Placement Strategy
DateOct. 29 - Nov. 1, 2018
Access Date2020, Dec. 04
Number of Files1
Size7135 KiB
Context area
Author1 Dias, Fábio Felix
2 Minghim, Rosane
Affiliation1 Instituto de Ciências Matemáticas e de Comutação (ICMC), University of São Paulo (USP)
2 Instituto de Ciências Matemáticas e de Comutação (ICMC), University of São Paulo (USP)
EditorRoss, Arun
Gastal, Eduardo S. L.
Jorge, Joaquim A.
Queiroz, Ricardo L. de
Minetto, Rodrigo
Sarkar, Sudeep
Papa, João Paulo
Oliveira, Manuel M.
Arbeláez, Pablo
Mery, Domingo
Oliveira, Maria Cristina Ferreira de
Spina, Thiago Vallin
Mendes, Caroline Mazetto
Costa, Henrique Sérgio Gutierrez
Mejail, Marta Estela
Geus, Klaus de
Scheer, Sergio
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
Conference LocationFoz do Iguaçu, PR, Brazil
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
History2018-09-04 01:55:48 :: -> administrator ::
2020-02-19 03:10:45 :: administrator -> :: 2018
Content and structure area
Is the master or a copy?is the master
Document Stagecompleted
Document Stagenot transferred
Content TypeExternal Contribution
Tertiary TypeFull Paper
Keywordsinformation visualization, multidimensional projection, multilevel projection.
AbstractThe 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.
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Target Filexhipp-extended-hierarchical.pdf
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Next Higher Units8JMKD3MGPAW/3RPADUS
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