1 resultado para Precipitate coarsening
em Bulgarian Digital Mathematics Library at IMI-BAS
Filtro por publicador
- Aberystwyth University Repository - Reino Unido (1)
- Acceda, el repositorio institucional de la Universidad de Las Palmas de Gran Canaria. España (1)
- AMS Tesi di Dottorato - Alm@DL - Università di Bologna (5)
- Aquatic Commons (2)
- ArchiMeD - Elektronische Publikationen der Universität Mainz - Alemanha (2)
- Archive of European Integration (2)
- Aston University Research Archive (22)
- B-Digital - Universidade Fernando Pessoa - Portugal (2)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (11)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (BDPI/USP) (6)
- Biblioteca Digital de Teses e Dissertações Eletrônicas da UERJ (3)
- Bioline International (1)
- BORIS: Bern Open Repository and Information System - Berna - Suiça (20)
- Brock University, Canada (3)
- Bucknell University Digital Commons - Pensilvania - USA (3)
- Bulgarian Digital Mathematics Library at IMI-BAS (1)
- CaltechTHESIS (2)
- Cambridge University Engineering Department Publications Database (4)
- CentAUR: Central Archive University of Reading - UK (27)
- Chinese Academy of Sciences Institutional Repositories Grid Portal (48)
- Cochin University of Science & Technology (CUSAT), India (3)
- Collection Of Biostatistics Research Archive (3)
- CORA - Cork Open Research Archive - University College Cork - Ireland (1)
- Deakin Research Online - Australia (48)
- Department of Computer Science E-Repository - King's College London, Strand, London (1)
- Digital Commons - Michigan Tech (4)
- Digital Commons - Montana Tech (2)
- Digital Commons at Florida International University (1)
- DigitalCommons - The University of Maine Research (2)
- DigitalCommons@The Texas Medical Center (5)
- eResearch Archive - Queensland Department of Agriculture; Fisheries and Forestry (1)
- FUNDAJ - Fundação Joaquim Nabuco (2)
- Greenwich Academic Literature Archive - UK (7)
- Helda - Digital Repository of University of Helsinki (3)
- Hospitais da Universidade de Coimbra (1)
- Indian Institute of Science - Bangalore - Índia (69)
- Instituto Politécnico de Castelo Branco - Portugal (1)
- National Center for Biotechnology Information - NCBI (7)
- Nottingham eTheses (2)
- Plymouth Marine Science Electronic Archive (PlyMSEA) (1)
- Publishing Network for Geoscientific & Environmental Data (59)
- QSpace: Queen's University - Canada (1)
- QUB Research Portal - Research Directory and Institutional Repository for Queen's University Belfast (29)
- Queensland University of Technology - ePrints Archive (25)
- Repositório Científico do Instituto Politécnico de Lisboa - Portugal (1)
- Repositório digital da Fundação Getúlio Vargas - FGV (1)
- REPOSITORIO DIGITAL IMARPE - INSTITUTO DEL MAR DEL PERÚ, Peru (1)
- Repositório Institucional da Universidade de Aveiro - Portugal (1)
- Repositório Institucional da Universidade de Brasília (1)
- Repositório Institucional da Universidade Estadual de São Paulo - UNESP (1)
- Repositório Institucional da Universidade Federal do Rio Grande - FURG (1)
- Repositorio Institucional de la Universidad de Málaga (2)
- Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho" (83)
- SAPIENTIA - Universidade do Algarve - Portugal (2)
- Universidad de Alicante (1)
- Universidad del Rosario, Colombia (2)
- Universidad Politécnica de Madrid (5)
- Universidade Complutense de Madrid (1)
- Universidade Federal do Pará (4)
- Universidade Federal do Rio Grande do Norte (UFRN) (12)
- Universidade Metodista de São Paulo (3)
- Universita di Parma (1)
- Universitat de Girona, Spain (1)
- Université de Montréal (1)
- Université de Montréal, Canada (6)
- Université Laval Mémoires et thèses électroniques (1)
- University of Michigan (3)
- University of Queensland eSpace - Australia (13)
- University of Washington (1)
- WestminsterResearch - UK (1)
Resumo:
In data mining, efforts have focused on finding methods for efficient and effective cluster analysis in large databases. Active themes of research focus on the scalability of clustering methods, the effectiveness of methods for clustering complex shapes and types of data, high-dimensional clustering techniques, and methods for clustering mixed numerical and categorical data in large databases. One of the most accuracy approach based on dynamic modeling of cluster similarity is called Chameleon. In this paper we present a modified hierarchical clustering algorithm that used the main idea of Chameleon and the effectiveness of suggested approach will be demonstrated by the experimental results.