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Revista Cubana de Ciencias Informáticas

On-line version ISSN 2227-1899

Abstract

AMADOR, Lisvandy; GARCIA, María M; GALVEZ LIO, Daniel  and  MAGDALENO, Damny. SemClustDML: algorithm to clustering scientific papers based on information provided by bibliographic references. Rev cuba cienc informat [online]. 2017, vol.11, n.2, pp. 46-60. ISSN 2227-1899.

ABSTRACT Data clustering has become one of the key forms of knowledge management. Particularly knowledge management from the scientific literature available on the internet is very importance for researchers, that why, specialized techniques have been developed in scientific articles clustering. The scientific publications follow a well-defined structure where there are fundamental parts that are always present as: title, abstract, keywords and bibliographical references. Specifically, the bibliographical references provide relevant information when determining whether two articles address similar topics. Therefore, to enhance the information provided by this subunit has a significant influence on the clustering´s result. The objective of this work was to develop a clustering algorithm that makes use of the special characteristics of the similarity matrix obtained with the SimRefBib function to improve the results of scientific articles clustering based on bibliographic references. The tests show that the proposed algorithm improves significantly the results of the grouping of scientific articles when it is based only on the information provided by the bibliographic references.

Keywords : Scientific Papers’ Clustering; Clustering’s algorithms; knowledge management.

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