New Similarity Function for Scientific Articles Clustering based on the Bibliographic References



Document title: New Similarity Function for Scientific Articles Clustering based on the Bibliographic References
Journal: Computación y Sistemas
Database: PERIÓDICA
System number: 000423264
ISSN: 1405-5546
Authors: 1
2
2
Institutions: 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
Year:
Season: Ene-Mar
Volumen: 22
Number: 1
Country: México
Language: Inglés
Document type: Artículo
Approach: Aplicado, descriptivo
English abstract 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
Disciplines: Bibliotecología y ciencia de la información
Keyword: Análisis y sistematización de la información,
Artículos científicos,
Función de semejanza,
Referencias bibliográficas,
Agrupamiento
Keyword: Information analysis,
Bibliographic references,
Scientific papers,
Similarity function,
Clustering
Full text: Texto completo (Ver HTML) Texto completo (Ver PDF)