Name
Title | ||
|---|---|---|
Utilization of global ranking information in GraphBased biomedical literature clustering |
Abstract | ||
|---|---|---|
In this paper, we explore how global ranking method in conjunction with local density method help identify meaningful term clusters from ontology enriched graph representation of biomedical literature corpus. One big problem with document clustering is how to discount the effects of class-unspecific general terms and strengthen the effects of class-specific core terms. We claim that a well constructed term graph can help improve the global ranking of classspecific core terms. We first apply PageRank and HITS to a directed abstracttitle term graph to target class specific core terms. Then k dense term clusters (graphs) are identified from these terms. Last, each document is assigned to its closest core term graph. A series of experiments are conducted on a document corpus collected from PubMed. Experimental results show that our approach is very effective to identify class-specific core terms and thus help document clustering. © Springer-Verlag Berlin Heidelberg 2007. |
Year | DOI | Venue |
|---|---|---|
2007 | null | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Keywords | Field | DocType |
Document clustering,Global ranking,Term graph | Cluster (physics),Ontology,Data mining,PageRank,Information retrieval,Ranking,Document clustering,Computer science,Abstract semantic graph,Cluster analysis,Graph (abstract data type) | Conference |
Volume | Issue | ISSN |
4654 LNCS | null | 16113349 |
ISBN | Citations | PageRank |
3-540-74552-1 | 0 | 0.34 |
References | Authors | |
6 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaodan Zhang | 1 | 25 | 8.37 |
| Xiaohua Hu | 2 | 2819 | 314.15 |
| Jiali Xia | 3 | 110 | 6.74 |
| Xiaohua Zhou | 4 | 0 | 0.34 |
| Palakorn Achananuparp | 5 | 302 | 23.16 |