1 resultado para 3D Computer Graphics
em Repository Napier
Filtro por publicador
- Repository Napier (1)
- Aberdeen University (2)
- Abertay Research Collections - Abertay University’s repository (1)
- Academic Archive On-line (Mid Sweden University; Sweden) (1)
- Acceda, el repositorio institucional de la Universidad de Las Palmas de Gran Canaria. España (9)
- AMS Tesi di Dottorato - Alm@DL - Università di Bologna (4)
- AMS Tesi di Laurea - Alm@DL - Università di Bologna (21)
- ArchiMeD - Elektronische Publikationen der Universität Mainz - Alemanha (2)
- Archive of European Integration (1)
- Aston University Research Archive (16)
- Biblioteca de Teses e Dissertações da USP (2)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (9)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (BDPI/USP) (66)
- Biblioteca Virtual del Sistema Sanitario Público de Andalucía (BV-SSPA), Junta de Andalucía. Consejería de Salud y Bienestar Social, Spain (2)
- Biodiversity Heritage Library, United States (3)
- BORIS: Bern Open Repository and Information System - Berna - Suiça (58)
- Brock University, Canada (1)
- Bulgarian Digital Mathematics Library at IMI-BAS (9)
- CentAUR: Central Archive University of Reading - UK (16)
- CiencIPCA - Instituto Politécnico do Cávado e do Ave, Portugal (19)
- Cochin University of Science & Technology (CUSAT), India (3)
- Consorci de Serveis Universitaris de Catalunya (CSUC), Spain (83)
- CORA - Cork Open Research Archive - University College Cork - Ireland (1)
- CUNY Academic Works (3)
- Dalarna University College Electronic Archive (1)
- DI-fusion - The institutional repository of Université Libre de Bruxelles (1)
- Digital Archives@Colby (2)
- Digital Commons - Michigan Tech (3)
- Digital Commons at Florida International University (11)
- Digital Peer Publishing (13)
- DigitalCommons@The Texas Medical Center (1)
- DigitalCommons@University of Nebraska - Lincoln (1)
- Doria (National Library of Finland DSpace Services) - National Library of Finland, Finland (6)
- DRUM (Digital Repository at the University of Maryland) (2)
- Duke University (3)
- FUNDAJ - Fundação Joaquim Nabuco (3)
- Galway Mayo Institute of Technology, Ireland (1)
- Greenwich Academic Literature Archive - UK (1)
- Instituto Politécnico do Porto, Portugal (32)
- Iowa Publications Online (IPO) - State Library, State of Iowa (Iowa), United States (10)
- Lume - Repositório Digital da Universidade Federal do Rio Grande do Sul (1)
- Martin Luther Universitat Halle Wittenberg, Germany (18)
- Massachusetts Institute of Technology (2)
- National Center for Biotechnology Information - NCBI (1)
- Nottingham eTheses (1)
- Plymouth Marine Science Electronic Archive (PlyMSEA) (2)
- Portal do Conhecimento - Ministerio do Ensino Superior Ciencia e Inovacao, Cape Verde (5)
- Publishing Network for Geoscientific & Environmental Data (3)
- QUB Research Portal - Research Directory and Institutional Repository for Queen's University Belfast (2)
- RDBU - Repositório Digital da Biblioteca da Unisinos (4)
- Repositório Científico da Universidade de Évora - Portugal (1)
- Repositório Científico do Instituto Politécnico de Lisboa - Portugal (20)
- Repositório da Produção Científica e Intelectual da Unicamp (1)
- Repositório Digital da UNIVERSIDADE DA MADEIRA - Portugal (3)
- Repositório Institucional da Universidade Estadual de São Paulo - UNESP (1)
- Repositorio Institucional de la Universidad de Málaga (1)
- Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho" (39)
- Repositorio Institucional Universidad EAFIT - Medelin - Colombia (3)
- Research Open Access Repository of the University of East London. (1)
- RUN (Repositório da Universidade Nova de Lisboa) - FCT (Faculdade de Cienecias e Technologia), Universidade Nova de Lisboa (UNL), Portugal (36)
- Scielo Saúde Pública - SP (16)
- Scielo Uruguai (1)
- Universidad de Alicante (19)
- Universidad Politécnica de Madrid (24)
- Universidade Complutense de Madrid (1)
- Universidade do Minho (33)
- Universidade Federal do Rio Grande do Norte (UFRN) (8)
- Universitat de Girona, Spain (25)
- Université de Lausanne, Switzerland (148)
- Université de Montréal (1)
- Université de Montréal, Canada (18)
- Université Laval Mémoires et thèses électroniques (1)
- University of Connecticut - USA (2)
- University of Michigan (37)
- University of Queensland eSpace - Australia (54)
- University of Southampton, United Kingdom (1)
- University of Washington (2)
- WestminsterResearch - UK (1)
- Worcester Research and Publications - Worcester Research and Publications - UK (1)
Resumo:
Choosing a single similarity threshold for cutting dendrograms is not sufficient for performing hierarchical clustering analysis of heterogeneous data sets. In addition, alternative automated or semi-automated methods that cut dendrograms in multiple levels make assumptions about the data in hand. In an attempt to help the user to find patterns in the data and resolve ambiguities in cluster assignments, we developed MLCut: a tool that provides visual support for exploring dendrograms of heterogeneous data sets in different levels of detail. The interactive exploration of the dendrogram is coordinated with a representation of the original data, shown as parallel coordinates. The tool supports three analysis steps. Firstly, a single-height similarity threshold can be applied using a dynamic slider to identify the main clusters. Secondly, a distinctiveness threshold can be applied using a second dynamic slider to identify “weak-edges” that indicate heterogeneity within clusters. Thirdly, the user can drill-down to further explore the dendrogram structure - always in relation to the original data - and cut the branches of the tree at multiple levels. Interactive drill-down is supported using mouse events such as hovering, pointing and clicking on elements of the dendrogram. Two prototypes of this tool have been developed in collaboration with a group of biologists for analysing their own data sets. We found that enabling the users to cut the tree at multiple levels, while viewing the effect in the original data, is a promising method for clustering which could lead to scientific discoveries.