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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3M5A64P
Repositorysid.inpe.br/sibgrapi/2016/07.20.18.35
Last Update2016:07.20.18.35.07 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2016/07.20.18.35.08
Metadata Last Update2022:06.14.00.08.29 (UTC) administrator
DOI10.1109/SIBGRAPI.2016.048
Citation KeyPagliosaPaglNona:2016:UnAtVa
TitleUnderstanding Attribute Variability in Multidimensional Projections
FormatOn-line
Year2016
Access Date2024, May 03
Number of Files1
Size19420 KiB
2. Context
Author1 Pagliosa, Lucas
2 Pagliosa, Paulo
3 Nonato, Luis Gustavo
Affiliation1 ICMC-USP
2 FACOM-UFMS
3 ICMC-USP
EditorAliaga, Daniel G.
Davis, Larry S.
Farias, Ricardo C.
Fernandes, Leandro A. F.
Gibson, Stuart J.
Giraldi, Gilson A.
Gois, João Paulo
Maciel, Anderson
Menotti, David
Miranda, Paulo A. V.
Musse, Soraia
Namikawa, Laercio
Pamplona, Mauricio
Papa, João Paulo
Santos, Jefersson dos
Schwartz, William Robson
Thomaz, Carlos E.
e-Mail Addresspagliosa@facom.ufms.br
Conference NameConference on Graphics, Patterns and Images, 29 (SIBGRAPI)
Conference LocationSão José dos Campos, SP, Brazil
Date4-7 Oct. 2016
PublisherIEEE Computer Society´s Conference Publishing Services
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2016-07-20 18:35:08 :: pagliosa@facom.ufms.br -> administrator ::
2016-10-05 14:49:14 :: administrator -> pagliosa@facom.ufms.br :: 2016
2016-10-14 19:59:23 :: pagliosa@facom.ufms.br -> administrator :: 2016
2022-06-14 00:08:29 :: administrator -> :: 2016
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordsattribute-based clustering
high-dimensional data visualization
interactive visual analysis
AbstractMultidimensional 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.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2016 > Understanding Attribute Variability...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Understanding Attribute Variability...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3M5A64P
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3M5A64P
Languageen
Target FilePID4370197.pdf
User Grouppagliosa@facom.ufms.br
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3M2D4LP
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2016/07.02.23.50 6
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume


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