| Revue: | Computación y Sistemas |
| Base de datos: | PERIÓDICA |
| Número de sistema: | 000423264 |
| ISSN: | 1405-5546 |
| Autores: | Amador Penichet, Lisvandy1 Magdaleno Guevara, Damny2 García Lorenzo, Maria Magdalena2 |
| Instituciones: | 1Universidad Central "Marta Abreu" de Las Villas, Instituto de Biotecnología de las Plantas, Santa Clara, Villa Clara. Cuba 2Universidad Central "Marta Abreu" de Las Villas, Departamento de Computación, Santa Clara, Villa Clara. Cuba |
| Año: | 2018 |
| Periodo: | Ene-Mar |
| Volumen: | 22 |
| Número: | 1 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Enfoque: | Aplicado, descriptivo |
| Resumen en inglés | The amount of scientific information available on the Internet, corporate intranets, and other media is growing rapidly. Managing knowledge from the information that can be found in scientific publications is essential for any researcher. The management of scientific information is increasingly more complex and challenging, since documents collections are generally heterogeneous, large, diverse and dynamic. Overcoming these challenges is essential to give to the scientists the best conditions to manage the time required to process scientific information. In this work, we implemented a new similarity’s function for scientific articles' clustering in based on the information provided by the references of the articles. The use of this function contributes significantly to discover relevant knowledge from scientific literature |
| Disciplinas: | Bibliotecología y ciencia de la información |
| Palabras clave: | Análisis y sistematización de la información, Artículos científicos, Función de semejanza, Referencias bibliográficas, Agrupamiento |
| Keyword: | Information analysis, Bibliographic references, Scientific papers, Similarity function, Clustering |
| Texte intégral: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
