Publicação: Icon and geometric data visualization with a self-organizing map grid
Carregando...
Data
Orientador
Coorientador
Pós-graduação
Curso de graduação
Título da Revista
ISSN da Revista
Título de Volume
Editor
Tipo
Trabalho apresentado em evento
Direito de acesso
Resumo
Data Visualization is an important tool for tasks related to Knowledge Discovery in Databases (KDD). Often the data to be visualized is complex, have multiple dimensions or features and consists of many individual data points, making visualization with traditional icon- and pixel-based and geometric techniques difficult. In this paper we propose a combination of icon-based and geometric-based visualization techniques backed up by a Self-Organizing Map, which allows dimensionality reduction and topology preservation. The technique is applied to some datasets of simple and intermediate complexity, and the results shows that it is possible to reduce clutter and facilitate identification of associations, clusters and outliers. © 2014 Springer International Publishing.
Descrição
Palavras-chave
Kohonen Self-organizing Maps, Visualization
Idioma
Inglês
Como citar
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 8584 LNCS, n. PART 6, p. 562-575, 2014.
