Medical document clustering using ontology-based term similarity measures

Xiaodan Zhang*, Liping Jing, Xiaohua Hu, Kwok Po NG, Jiali Xia, Xiaohua Zhou

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

25 Citations (Scopus)


Recent research shows that ontology as background knowledge can improve document clustering quality with its concept hierarchy knowledge. Previous studies take term semantic similarity as an important measure to incorporate domain knowledge into clustering process such as clustering initialization and term re-weighting. However, not many studies have been focused on how different types of term similarity measures affect the clustering performance for a certain domain. In this article, we conduct a comparative study on how different term semantic similarity measures including path-based information-content-based and feature-based similarity measure affect document clustering. Term re-weighting of document vector is an important method to integrate domain ontology to clustering process. In detail, the weight of a term is augmented by the weights of its co-occurred concepts. Spherical k-means are used for evaluate document vector re-weighting on two real-world datasets: Disease10 and OHSUMED23. Erperimemal results on nine different semantic measures have shown that: (1) there is no certain type of similarity measures that significantly outperforms the others; (2) Several similarity measures have rather more stable performance than the others; (3) term re-weighting has positive effects on medical document clustering, but might not be significant when documents are short of terms.

Original languageEnglish
Pages (from-to)62-73
Number of pages12
JournalInternational Journal of Data Warehousing and Mining
Issue number1
Publication statusPublished - 2008

Scopus Subject Areas

  • Software
  • Hardware and Architecture

User-Defined Keywords

  • Data warehouse
  • Decision support systems-DSS
  • Multidimensional query language
  • OLAP algebra


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