Textquest: document clustering of Medline abstracts for concept discovery in molecular biology.
We present an algorithm for large-scale document clustering of biological text, obtained from Medline abstracts. The algorithm is based on statistical treatment of terms, stemming, the idea of a 'go-list', unsupervised machine learning and graph layout optimization. The method is flexible and robust, controlled by a small number of parameter values. Experiments show that the resulting document clusters are meaningful as assessed by cluster-specific terms. Despite the statistical nature of the approach, with minimal semantic analysis, the terms provide a shallow description of the document corpus and support concept discovery.
Iliopoulos
I
I
Computational Genomics Group, The European Bioinformatics Institute, EMBL Cambridge Outstation, Cambridge CB10 1SD, UK. ioannis@ebi.ac.uk
Enright
A J
AJ
Ouzounis
C A
CA
D000043:Abstracting and Indexing; D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D016000:Cluster Analysis; D004330:Drosophila / Q000196:embryology; D016239:MEDLINE; D008967:Molecular Biology; D009626:Terminology as Topic