Feature diversity in cluster ensembles for robust document clustering

Producción científica: Capítulo del libroContribución a congreso/conferenciarevisión exhaustiva

13 Citas (Scopus)

Resumen

The performance of document clustering systems depends on employing optimal text representations, which are not only difficult to determine beforehand, but also may vary from one clustering problem to another. As a first step towards building robust document clusterers, a strategy based on feature diversity and cluster ensembles is presented in this work. Experiments conducted on a binary clustering problem show that our method is robust to near-optimal model order selection and able to detect constructive interactions between different document representations in the test bed.

Idioma originalInglés
Título de la publicación alojadaProceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
EditorialAssociation for Computing Machinery (ACM)
Páginas697-698
Número de páginas2
ISBN (versión impresa)1595933697, 9781595933690
DOI
EstadoPublicada - 2006
Evento29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval - Seatttle, WA, Estados Unidos
Duración: 6 ago 200611 ago 2006

Serie de la publicación

NombreProceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
Volumen2006

Conferencia

Conferencia29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
País/TerritorioEstados Unidos
CiudadSeatttle, WA
Período6/08/0611/08/06

Huella

Profundice en los temas de investigación de 'Feature diversity in cluster ensembles for robust document clustering'. En conjunto forman una huella única.

Citar esto