Quantitative methods of standardization in cluster analysis

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dc.contributor.authorNOGUEIRA, A.L.pt_BR
dc.contributor.authorMUNITA, C.S.pt_BR
dc.contributor.authorCOSTA, A.F.pt_BR
dc.contributor.authorGOMES, D.M.C.pt_BR
dc.contributor.authorKIPNIS, R.pt_BR
dc.contributor.editorACHARYA, R.pt_BR
dc.contributor.editorSWAIN, K.K.pt_BR
dc.contributor.editorSATHYAPRIYA, R.S.pt_BR
dc.contributor.editorREDDY, A.V.R.pt_BR
dc.contributor.editorPUJARI, P.K.pt_BR
dc.coverageInternacionalpt_BR
dc.creator.eventoINTERNATIONAL CONFERENCE ON MODERN TRENDS IN ACTIVATION ANALYSIS, 15thpt_BR
dc.date.accessioned2020-04-01T16:33:19Z
dc.date.available2020-04-01T16:33:19Z
dc.date.eventoNovember 17-22, 2019pt_BR
dc.description.abstractThe archaeological study of ceramics using multi-elemental analytical techniques such as instrumental neutron activation analysis (INAA) is important in archaeology due to its potential to identify the raw materials used in their manufacture,and subsequently help to infer the degree of interaction among ancient communities. Several multivariate statistical methods are used in chemical composition data analysis, such as principal component analysis, cluster analysis and discriminant analysis. Pattern recognition approaches are divided into unsupervised learning and supervised learning. Cluster analysis is a technique for pattern recognition and is an unsupervised approach. When applying cluster analysis, raw data, or actual measurements, are not used directly. Thus, a problem that arises during cluster analysis involves the decision of whether or not to standardize the input variables before calculating measures of distance. The standardizationof variables is necessary in cases where the measure of dissimilarity, such as the Euclidean distance, is sensitive to differences in the magnitudes or scales of the input variables. The aim of this paper is to assess the impact, and evaluate the usefulness of three standardization techniques in determining the number of clusters for a data set of 140 ceramic fragments from eight archaeological sites from the upper Madeira river, Rondônia, Brazil, in which Na, K, La, Sm, Yb, Lu, Sc, Cr, Fe, Co, Zn, Rb, Cs, Ce, Eu,Hf, Ta, and Th mass fractions were determined by INAA.pt_BR
dc.event.siglaMTAApt_BR
dc.format.extent76-76pt_BR
dc.identifier.citationNOGUEIRA, A.L.; MUNITA, C.S.; COSTA, A.F.; GOMES, D.M.C.; KIPNIS, R. Quantitative methods of standardization in cluster analysis: finding groups in data. In: ACHARYA, R. (ed.); SWAIN, K.K. (ed.); SATHYAPRIYA, R.S. (ed.); REDDY, A.V.R. (ed.); PUJARI, P.K. (ed.). In: INTERNATIONAL CONFERENCE ON MODERN TRENDS IN ACTIVATION ANALYSIS, 15th, November 17-22, 2019, Mumbai, India. <b>Abstract...</b> Mumbai, India: Bhabha Atomic Research Centre, 2019. p. 76-76. Disponível em: http://repositorio.ipen.br/handle/123456789/31026.
dc.identifier.orcid0000-0003-0546-1044pt_BR
dc.identifier.urihttp://repositorio.ipen.br/handle/123456789/31026
dc.localMumbai, Indiapt_BR
dc.local.eventoMumbai, Indiapt_BR
dc.publisherBhabha Atomic Research Centrept_BR
dc.rightsopenAccesspt_BR
dc.titleQuantitative methods of standardization in cluster analysispt_BR
dc.typeResumo de eventos científicospt_BR
dspace.entity.typePublication
ipen.autorCASIMIRO JAYME ALFREDO SEPULVEDA MUNITA
ipen.codigoautor1325
ipen.contributor.ipenauthorCASIMIRO JAYME ALFREDO SEPULVEDA MUNITA
ipen.date.recebimento20-04
ipen.event.datapadronizada2019pt_BR
ipen.identifier.ipendoc26821pt_BR
ipen.notas.internasAbstractpt_BR
ipen.subtitulofinding groups in datapt_BR
ipen.type.genreResumo
relation.isAuthorOfPublication9f2b42a5-30f7-4805-96a1-1cde2b1a405b
relation.isAuthorOfPublication.latestForDiscovery9f2b42a5-30f7-4805-96a1-1cde2b1a405b
sigepi.autor.atividadeMUNITA, C.S.:1325:320:Npt_BR
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