A Graph-Based Biomedical Literature Clustering Approach Utilizing Term's Global and Local Importance Information

Abstract

In this article, we present a graph-based knowledge representation for biomedical digital library literature clustering An efficient clustering method is developed to identify the ontology-enriched k-highest density term subgraphs that capture the core semantic relationship information about each document cluster The distance between each document and the k term graph clusters is calculated. A document is then assigned to the closest term cluster. The extensive experimental results on two PubMed document sets (Disease10 and OHSUMED23) show that our approach is comparable to spherical k-means. The contributions of our approach are the following: (1) we provide two corpus-level graph representations to improve document clustering, a term co-occurrence graph and an abstract title graph; (2) we develop an efficient and effective document clustering algorithm by udentifying k distinguishable class-specific core term subgraphs using terms’global and local importance information; and (3) the identified term clusters give a meaningful explanation for the document clustering results.

Publication
International Journal of Data Warehousing and Mining