Analysis of standard clustering algorithms for grouping MEDLINE abstracts into evidence-based medicine intervention categories

Abstract

The paper describes a process of clustering of article abstracts, taken from the largest bibliographic life sciences and biomedical information MEDLINE database into categories that correspond to types of medical interventions - types of patient treatments. Experiments were carried out to evaluate the quality of clustering for the following algorithms: K-means; K-means++; Hierarchical clustering, SIB (Sequential information bottleneck) together with the LSA (Latent Semantic Analysis) methods and MI (Mutual Information) which allow selecting feature vectors. Best results of clustering were achieved by K-means++ together with LSA then 210-dimensional space was chosen: Purity = 0.5719, Entropy = 1.3841, Normalized Entropy = 0.6299.