Ontology-based semantic smoothing model for biomedical document clustering
S. Logeswari, K. Premalatha · International Journal of Telemedicine and Clinical Practices · 2015
One of the major issues of data mining is the clustering of unstructured text documents. Traditional clustering algorithms are failing to prove the accuracy of the clustering process because of the characteristics of text documents such as high dimension, complex semantics, sparsity, etc. Recent researches focus on the clustering of text documents based on the semantic smoothing technique, which resolves the conflicts by general words and the sparsity of class-specific core words. In this work ontology-based semantic smoothing model is proposed which uses the domain ontology for concept extraction. It is a mixture of simple language model and a topic signature translation model. The results obtained from the proposed method shows a significant improvement in the clustering process than the existing methods in terms of cluster quality.