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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3RMKREB
Repositorysid.inpe.br/sibgrapi/2018/08.24.16.54
Last Update2018:08.24.16.54.46 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2018/08.24.16.54.46
Metadata Last Update2022:06.14.00.09.06 (UTC) administrator
DOI10.1109/SIBGRAPI.2018.00060
Citation KeyMonteroFalc:2018:DiClAp
TitleA Divide-and-Conquer Clustering Approach based on Optimum-Path Forest
FormatOn-line
Year2018
Access Date2024, May 02
Number of Files1
Size3527 KiB
2. Context
Author1 Montero, Adán Echemendía
2 Falcão, Alexandre Xavier
Affiliation1 Laboratory of Image Data Science, Institute of Computing, University of Campinas
2 Laboratory of Image Data Science, Institute of Computing, University of Campinas
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
e-Mail Addressaemontero7@gmail.com
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
Conference LocationFoz do Iguaçu, PR, Brazil
Date29 Oct.-1 Nov. 2018
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2018-08-24 16:54:46 :: aemontero7@gmail.com -> administrator ::
2022-06-14 00:09:06 :: administrator -> :: 2018
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordsclustering
optimum-path forest
image segmentation
image foresting transform
divide-and-conquer
AbstractData clustering is one of the main challenges when solving Data Science problems. Despite its progress over almost one century of research, clustering algorithms still fail in identifying groups naturally related to the semantics of the problem. Moreover, the technological advances add crucial challenges with a considerable data increase, which are not handled by most techniques. We address these issues by proposing a divide-and-conquer approach to a clustering technique, which is unique in finding one group per dome of the probability density function of the data --- the Optimum-Path Forest (OPF) clustering algorithm. Our approach can use all samples, or at least many samples, in the unsupervised learning process without affecting the grouping performance and, therefore, being less likely to lose relevant grouping information. We show that it can obtain satisfactory results when segmenting natural images into superpixels.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2018 > A Divide-and-Conquer Clustering...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > A Divide-and-Conquer Clustering...
doc Directory Contentaccess
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3RMKREB
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3RMKREB
Languageen
Target File34.pdf
User Groupaemontero7@gmail.com
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3RPADUS
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2018/09.03.20.37 8
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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