Clustering Analysis for Vasculitic Diseases

  • Pınar Yıldırım
  • Çınar Çeken
  • Kağan Çeken
  • Mehmet R. Tolun
Part of the Communications in Computer and Information Science book series (CCIS, volume 88)


We introduce knowledge discovery for vasculitic diseases in this paper. Vasculitic diseases affect some organs and tissues and diagnosing can be quite difficult. Biomedical literature can contain hidden and useful knowledge for biomedical research and we develop a study based on co-occurrence analysis by using the articles in MEDLINE which is a widely used database.The mostly seen vasculitic diseases are selected to explore hidden patterns. We select PolySearch system as a web based biomedical text mining tool to find organs and tissues in the articles and create two separate datasets with their frequencies for each disease. After forming these datasets, we apply hierarchical clustering analysis to find similarities between the diseases. Clustering analysis reveals some similarities between diseases. We think that the results of clustered diseases positively affect on the medical research of vasculitic diseases especially during the diagnosis and certain similarities can provide different views to medical specialists.


Biomedical text mining data mining clustering analysis vasculitic diseases 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Pınar Yıldırım
    • 1
  • Çınar Çeken
    • 2
  • Kağan Çeken
    • 3
  • Mehmet R. Tolun
    • 1
  1. 1.Faculty of Engineering and Architecture, Department of Computer EngineeringÇankaya UniversityAnkaraTurkey
  2. 2.Department of Physical Medicine and RehabilitationThe Ministry of Health of Turkey Antalya Education and Research HospitalAntalyaTurkey
  3. 3.Faculty of Medicine, Department of RadiologyAkdeniz UniversityArapsuyuTurkey

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