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: Chapter in book/report/conference proceedingChapterpeer-review

4 Citations (Scopus)

Abstract

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 reweighting on two real-world datasets: Disease10 and OHSUMED23. Experimental 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
Title of host publicationMedical Informatics
Subtitle of host publicationConcepts, Methodologies, Tools, and Applications
PublisherIGI Global
Pages2232-2243
Number of pages12
Volume4-4
ISBN (Electronic)9781605660516
ISBN (Print)1605660507, 9781605660509
DOIs
Publication statusPublished - 30 Sep 2008

Scopus Subject Areas

  • Engineering(all)
  • Social Sciences(all)

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