Evaluating fuzzy clustering for relevance-based information access

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2003

Autores

Maria Eduarda S. Mendes (Autor) FEUP; L. Sacks (Autor) Outra

Ata de Conferência Internacional

Título: IEEE International Conference on Fuzzy Systems, FUZZ-IEEE'2003

Páginas: 648 -653

St. Louis, Missouri, USA, May 2003

Indexação

ISI Proceedings

INSPEC

Classificação Científica

FOS: Ciências da engenharia e tecnologias > Engenharia electrotécnica, electrónica e informática

Outras Informações

Abstract (EN): This paper analyzes the suitability of fuzzy clustering methods for the discovery of relevant document relationships, motivated by the need for enhanced relevance-based navigation of Web-accessible resources. The performance evaluation of a modified Fuzzy c-Means algorithm is carried out, and a comparison with a traditional hard clustering technique is presented. Clustering precision and recall are defined and applied as quantitative evaluation measures of the clustering results. The experiments with various test document sets have shown that in most cases fuzzy clustering performs better than the hard k-Means algorithm and that the fuzzy membership values can be used to determine document relevance and to control the amount of information retrieved to the user.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 6