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dc.contributor.CRUESPUNIVERSIDADE DE ESTADUAL DE CAMPINASpt_BR
dc.identifier.isbn978-1-4673-8942-6pt
dc.contributor.authoremailbruno.medina@pos.ft.unicamp.br; w137897@dac.unicamp.br; m147207@dac.unicamp.br; celmar@ft.unicamp.brpt_BR
dc.typeCongressopt_BR
dc.titleSmoothed Multiple Binarization - Using Pqr Tree, Smoothing, Feature Vectors And Thresholding For Matrix Reorderingen
dc.contributor.authorMedinapt_BR
dc.contributor.authorBruno F.; Kawakamipt_BR
dc.contributor.authorWillian H.; da Silvapt_BR
dc.contributor.authorMaressa R.; da Silvapt_BR
dc.contributor.authorCelmar G.pt_BR
unicamp.authorda Silva, Celmar G.] Univ Estadual Campinas, Sch Technol, Campinas, SP, Brazilpt_BR
unicamp.author.external[Medina, Bruno F.pt_BR
unicamp.author.externalKawakami, Willian H.pt_BR
unicamp.author.externalda Silva, Maressa R.pt_BR
dc.subjectMatrix Reorderingen
dc.subjectReordering Algorithmen
dc.subjectPqr-treeen
dc.subjectPattern Recognitionen
dc.description.abstractFinding appropriate permutations of rows and columns of a matrix may help users to see hidden patterns in datasets. This paper presents a set of binarization-based matrix reordering algorithms able to reveal some patterns in a quantitative data set. In these algorithms, matrix binarization converts a matrix into a set of binary ones, from which the algorithms calculate desired groups of similar rows and columns. PQR trees provide a linear order of rows and columns that obey these groups as much as possible. These algorithms use mean or median filter as smoothing techniques to minimize data noise in intermediate matrix permutation steps. They also use feature vectors or thresholding for defining binarization thresholds in intermediate steps. Our experiments with synthetic matrices revealed that our algorithms are competitive with other matrix reordering algorithms in terms of quality of reordering (Moore stress) and runtime. We observed that our set of algorithms is suitable to reveal Circumplex pattern with all tested noise ratios, and other data canonical patterns with low noise ratio.en
dc.relation.ispartofProceedings 2016 20th International Conference Information Visualisation IV 2016pt_BR
dc.publisherIEEEpt_BR
dc.publisherNew Yorkpt_BR
dc.date.issued2016pt_BR
dc.identifier.citationProceedings 2016 20th International Conference Information Visualisation Iv 2016. Ieee, p. 88 - 93, 2016.pt_BR
dc.language.isoEnglishpt_BR
dc.description.firstpage88pt_BR
dc.description.lastpage93pt_BR
dc.rightsfechadopt_BR
dc.sourceWOSpt_BR
dc.identifier.issn1550-6037pt_BR
dc.identifier.wosidWOS:000389494200015pt_BR
dc.identifier.doi10.1109/IV.2016.22pt_BR
dc.identifier.urlhttp://ieeexplore.ieee.org/document/7557909/pt_BR
dc.date.available2017-11-13T13:22:39Z-
dc.date.accessioned2017-11-13T13:22:39Z-
dc.description.provenanceMade available in DSpace on 2017-11-13T13:22:39Z (GMT). No. of bitstreams: 1 000389494200015.pdf: 524797 bytes, checksum: e4fb83e69beeee27c129287778f6cc13 (MD5) Previous issue date: 2016en
dc.identifier.urihttp://repositorio.unicamp.br/jspui/handle/REPOSIP/327940-
dc.description.conferencenome20th International Conference Information Visualisation (IV)pt_BR
dc.description.conferencedateJUL 19-22, 2016pt_BR
dc.description.conferencelocationUniv NOVA Lisboa, Lisbon, PORTUGALpt_BR
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