Efficient Semisupervised MEDLINE Document Clustering With MeSH-Semantic and Global-Content Constraints

• Published in: IEEE transactions on cybernetics Vol. 43; no. 4; pp. 1265 - 1276
• Main Authors: Jun Gu, Wei Feng, Jia Zeng, Mamitsuka, Hiroshi, Shanfeng Zhu
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.08.2013; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Analogies; Bioinformatics; Biomedical text mining; Cluster Analysis; Clustering; Clustering algorithms; Collection; Cybernetics; Data Mining - methods; document clustering; Educational institutions; Empirical analysis; Genomics; Indexing; Medical Subject Headings; MEDLINE; Representations; Semantics; semisupervised clustering; Spectra; spectral clustering; Studies; Supervised Machine Learning; Thesauri; Vectors

For clustering biomedical documents, we can consider three different types of information: the local-content (LC) information from documents, the global-content (GC) information from the whole MEDLINE collections, and the medical subject heading (MeSH)-semantic (MS) information. Previous methods for...