Hybrid self-optimized clustering model based on citation links and textual features to detect research topics.

ABSTRACT: The challenge of detecting research topics in a specific research field has attracted attention from researchers in the bibliometrics community. In this study, to solve two problems of clustering papers, i.e., the influence of different distributions of citation links and involved textual features on similarity computation, the authors propose a hybrid self-optimized clustering model to detect research topics by extending the hybrid clustering model to identify "core documents". First, the Amsler network, consisting of bibliographic coupling and co-citation links, is created to calculate the citation-based similarity based on the cosine angle of papers. Second, the cosine similarity is also used to compute the text-based similarity, which consists of the textual statistical and topological

SUBMITTER: Yu D 

PROVIDER: S-EPMC5659815 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature