Clustering of biomedical documents using ontology-based TF-IGM enriched semantic smoothing model for telemedicine applications

Clustering of biomedical documents has become a vital research concept due to its importance in the clinical and telemedicine applications. The clustering of the medical documents is being considered as a major issue because of its unstructured nature. This paper focuses on developing an efficient document clustering approach for the medical documents to be utilized in telemedicine applications. Most existing models utilize n-gram techniques for phrase identification and term, concept or semantic based models for clustering applications. However n-gram does not perform well when the original document has been modified while only hybrid models provide relatively improved clustering. The proposed document clustering approach is named as enriched semantic smoothing model which has been developed on the concept of Mesh ontology. As the semantic smoothing model is not effective in handling the density of general words, an improved model with term frequency and inverse gravity moment (TF-IGM) factor and improved background elimination is used. Unlike term frequency and inverse document frequency), TF-IGM precisely measure the class distinguishing power of a term by making use of the fine-grained term distribution across different classes of text in documents. The modified n-gram technique, which detects the cases of substitution and deletion in the documents and averts them, improves the phrases identification. The clustering efficiency of the k-means clustering and hierarchical clustering algorithms is improved by utilizing the proposed model. The experiments are made on Mesh ontology based PubMed documents with similarity measures and cluster validity indexes used for comparisons. The results show that the proposed approach of medical document clustering is highly accurate and thus improves the concepts of clinical practices and telemedicine.