Journal article
A Graph-Based Biomedical Literature Clustering Approach Utilizing Term's Global and Local Importance Information
International journal of data warehousing and mining, v 4(4), pp 84-101
01 Oct 2008
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 identifying 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.
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15 Record Views
3 citations in Scopus
Details
- Title
- A Graph-Based Biomedical Literature Clustering Approach Utilizing Term's Global and Local Importance Information
- Creators
- Xiaodan Zhang - Drexel UniversityXiaohua Hu - Jiangxi University of Finance and EconomicsJiali Xia - Jiangxi University of Finance and EconomicsXiaohua Zhou - Drexel UniversityPalakorn Achananuparp - Drexel University
- Publication Details
- International journal of data warehousing and mining, v 4(4), pp 84-101
- Number of pages
- 18
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science (Informatics)
- Scopus ID
- 2-s2.0-47349102425
- Other Identifier
- 991019173436604721