1 resultado para Minimum SNPs
em Repositório Científico do Instituto Politécnico de Lisboa - Portugal
Filtro por publicador
- Academic Research Repository at Institute of Developing Economies (1)
- AMS Tesi di Dottorato - Alm@DL - Università di Bologna (5)
- Aquatic Commons (5)
- ArchiMeD - Elektronische Publikationen der Universität Mainz - Alemanha (1)
- Archive of European Integration (35)
- Archivo Digital para la Docencia y la Investigación - Repositorio Institucional de la Universidad del País Vasco (4)
- Aston University Research Archive (7)
- Biblioteca Digital | Sistema Integrado de Documentación | UNCuyo - UNCUYO. UNIVERSIDAD NACIONAL DE CUYO. (1)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (4)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (BDPI/USP) (4)
- Biblioteca Digital de Teses e Dissertações Eletrônicas da UERJ (1)
- BORIS: Bern Open Repository and Information System - Berna - Suiça (41)
- Boston University Digital Common (3)
- Bucknell University Digital Commons - Pensilvania - USA (2)
- Bulgarian Digital Mathematics Library at IMI-BAS (4)
- CaltechTHESIS (1)
- Cambridge University Engineering Department Publications Database (52)
- CentAUR: Central Archive University of Reading - UK (49)
- Chinese Academy of Sciences Institutional Repositories Grid Portal (15)
- Cochin University of Science & Technology (CUSAT), India (5)
- Collection Of Biostatistics Research Archive (1)
- Comissão Econômica para a América Latina e o Caribe (CEPAL) (1)
- CORA - Cork Open Research Archive - University College Cork - Ireland (1)
- Corvinus Research Archive - The institutional repository for the Corvinus University of Budapest (4)
- Deakin Research Online - Australia (40)
- Department of Computer Science E-Repository - King's College London, Strand, London (4)
- Digital Commons - Michigan Tech (1)
- Digital Commons - Montana Tech (1)
- Digital Commons at Florida International University (4)
- Digital Peer Publishing (1)
- DigitalCommons@The Texas Medical Center (3)
- Duke University (1)
- eResearch Archive - Queensland Department of Agriculture; Fisheries and Forestry (4)
- FUNDAJ - Fundação Joaquim Nabuco (1)
- Gallica, Bibliotheque Numerique - Bibliothèque nationale de France (French National Library) (BnF), France (4)
- Helda - Digital Repository of University of Helsinki (4)
- Indian Institute of Science - Bangalore - Índia (53)
- Institute of Public Health in Ireland, Ireland (3)
- INSTITUTO DE PESQUISAS ENERGÉTICAS E NUCLEARES (IPEN) - Repositório Digital da Produção Técnico Científica - BibliotecaTerezine Arantes Ferra (2)
- Instituto Politécnico de Bragança (1)
- Instituto Politécnico de Leiria (1)
- Iowa Publications Online (IPO) - State Library, State of Iowa (Iowa), United States (2)
- Ministerio de Cultura, Spain (2)
- National Center for Biotechnology Information - NCBI (3)
- Plymouth Marine Science Electronic Archive (PlyMSEA) (3)
- Publishing Network for Geoscientific & Environmental Data (51)
- QUB Research Portal - Research Directory and Institutional Repository for Queen's University Belfast (32)
- Queensland University of Technology - ePrints Archive (218)
- RCAAP - Repositório Científico de Acesso Aberto de Portugal (1)
- Repositório Alice (Acesso Livre à Informação Científica da Embrapa / Repository Open Access to Scientific Information from Embrapa) (1)
- Repositório Científico da Universidade de Évora - Portugal (1)
- Repositório Científico do Instituto Politécnico de Lisboa - Portugal (1)
- Repositório digital da Fundação Getúlio Vargas - FGV (2)
- Repositório Institucional da Universidade Estadual de São Paulo - UNESP (1)
- Repositorio Institucional de la Universidad de La Laguna (1)
- Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho" (74)
- Repositorio Institucional Universidad de Medellín (2)
- SAPIENTIA - Universidade do Algarve - Portugal (1)
- Savoirs UdeS : plateforme de diffusion de la production intellectuelle de l’Université de Sherbrooke - Canada (1)
- School of Medicine, Washington University, United States (4)
- Scielo España (1)
- Universidad Autónoma de Nuevo León, Mexico (2)
- Universidad de Alicante (1)
- Universidad del Rosario, Colombia (1)
- Universidad Politécnica de Madrid (20)
- Universidade Complutense de Madrid (1)
- Universidade de Lisboa - Repositório Aberto (1)
- Universidade Federal do Rio Grande do Norte (UFRN) (2)
- Universitat de Girona, Spain (2)
- Université de Lausanne, Switzerland (2)
- Université de Montréal (1)
- Université de Montréal, Canada (14)
- University of Connecticut - USA (1)
- University of Michigan (114)
- University of Queensland eSpace - Australia (11)
- University of Southampton, United Kingdom (1)
- WestminsterResearch - UK (1)
- Worcester Research and Publications - Worcester Research and Publications - UK (1)
Resumo:
Research on cluster analysis for categorical data continues to develop, new clustering algorithms being proposed. However, in this context, the determination of the number of clusters is rarely addressed. We propose a new approach in which clustering and the estimation of the number of clusters is done simultaneously for categorical data. We assume that the data originate from a finite mixture of multinomial distributions and use a minimum message length criterion (MML) to select the number of clusters (Wallace and Bolton, 1986). For this purpose, we implement an EM-type algorithm (Silvestre et al., 2008) based on the (Figueiredo and Jain, 2002) approach. The novelty of the approach rests on the integration of the model estimation and selection of the number of clusters in a single algorithm, rather than selecting this number based on a set of pre-estimated candidate models. The performance of our approach is compared with the use of Bayesian Information Criterion (BIC) (Schwarz, 1978) and Integrated Completed Likelihood (ICL) (Biernacki et al., 2000) using synthetic data. The obtained results illustrate the capacity of the proposed algorithm to attain the true number of cluster while outperforming BIC and ICL since it is faster, which is especially relevant when dealing with large data sets.