| Revista: | Polibits |
| Base de datos: | PERIÓDICA |
| Número de sistema: | 000373737 |
| ISSN: | 1870-9044 |
| Autores: | Cobos, Carlos1 Mendoza, Martha1 León, Elizabeth2 Manic, Milos3 Herrera Viedma, Enrique4 |
| Instituciones: | 1Universidad del Cauca, Cali, Valle del Cauca. Colombia 2Universidad Nacional de Colombia, Bogotá. Colombia 3University of Idaho, Idaho Falls, Idaho. Estados Unidos de América 4Universidad de Granada, Granada. España |
| Año: | 2012 |
| Periodo: | Jul-Dic |
| Número: | 46 |
| Paginación: | 31-45 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Enfoque: | Experimental, aplicado |
| Resumen en inglés | As resources become more and more available on the Web, so the difficulties associated with finding the desired information increase. Intelligent agents can assist users in this task since they can search, filter and organize information on behalf of their users. Web document clustering techniques can also help users to find pages that meet their information requirements. This paper presents a personalized web document clustering called TopicSearch. TopicSearch introduces a novel inverse document frequency function to improve the query expansion process, a new memetic algorithm for web document clustering, and frequent phrases approach for defining cluster labels. Each user query is handled by an agent who coordinates several tasks including query expansion, search results acquisition, preprocessing of search results, cluster construction and labeling, and visualization. These tasks are performed by specialized agents whose execution can be parallelized in certain instances. The model was successfully tested on fifty DMOZ datasets. The results demonstrated improved precision and recall over traditional algorithms (k-means, Bisecting k-means, STC y Lingo). In addition, the presented model was evaluated by a group of twenty users with 90% being in favor of the model |
| Disciplinas: | Ciencias de la computación |
| Palabras clave: | Redes, Recuperación de información, Agrupamiento de documentos, Agentes inteligentes, Expansión de búsquedas, Algoritmos, Perfil de usuario |
| Keyword: | Computer science, Networks, Information retrieval, Document clustering, Intelligent agents, Query expansion, Algorithms, User profile |
| Texto completo: | Texto completo (Ver HTML) |
