title: Full text clustering and relationship network analysis of biomedical publications.
abstract: Rapid developments in the biomedical sciences have increased the demand for automatic clustering of biomedical publications. In contrast to current approaches to text clustering, which focus exclusively on the contents of abstracts, a novel method is proposed for clustering and analysis of complete biomedical article texts. To reduce dimensionality, Cosine Coefficient is used on a sub-space of only two vectors, instead of computing the Euclidean distance within the space of all vectors. Then a strategy and algorithm is introduced for Semi-supervised Affinity Propagation (SSAP) to improve analysis efficiency, using biomedical journal names as an evaluation background. Experimental results show that by avoiding high-dimensional sparse matrix computations, SSAP outperforms conventional k-means methods and improves upon the standard Affinity Propagation algorithm. In constructing a directed relationship network and distribution matrix for the clustering results, it can be noted that overlaps in scope and interests among BioMed publications can be easily identified, providing a valuable analytical tool for editors, authors and readers.
authors.affiliation: College of Computer Science and Technology, Jilin University, Changchun, China; State Key Laboratory of Inorganic Synthesis and Preparative Chemistry, College of Chemistry, Jilin University, Changchun, China.
authors.forename: Renchu
authors.initials: R
authors.lastname: Guan
authors.affiliation: College of Earth Sciences, Jilin University, Changchun, China.
authors.forename: Chen
authors.initials: C
authors.lastname: Yang
authors.affiliation: Department of Information Engineering and Computer Science, University of Trento, Trento, Italy.
authors.forename: Maurizio
authors.initials: M
authors.lastname: Marchese
authors.affiliation: College of Computer Science and Technology, Jilin University, Changchun, China.
authors.forename: Yanchun
authors.initials: Y
authors.lastname: Liang
authors.affiliation: College of Computer Science and Technology, Jilin University, Changchun, China.
authors.forename: Xiaohu
authors.initials: X
authors.lastname: Shi
mesh_terms: D016000:Cluster Analysis; D011643:Publishing