Browsing by Subject "biomedical measurement"

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    Comparison of Deep Learning based Concept Representations for Biomedical Document Clustering
    (IEEE, 2018) Shah, Setu; Luo, Xiao; Computer and Information Science, School of Science
    In this research, document representations based on distributed representations of the concepts along with new weighting schemes for the documents are explored. The baseline weighting scheme is the traditional Term Frequency-Inverse Document Frequency (TF-IDF) of the concepts, whereas, the other two newly proposed ones consider both local content using the TF-IDF and associations between concepts. The distributed representations of the concepts are measured using a deep learning algorithm. The evaluation of the proposed document representations is based on the k-means clustering results. The results show that document representation based on TF-IDF in combination with the term based distributed representations for concepts outperforms the other two based on the returned evaluation metrics - F1-measure (80.21%) and Purity (77.1%).
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    Exploring diseases based biomedical document clustering and visualization using self-organizing maps
    (IEEE, 2017-10) Shah, Setu; Luo, Xiao; Computer and Information Science, School of Science
    Document clustering is a text mining technique used to provide better document search and browsing in digital libraries or online corpora. In this research, a vector representation of concepts of diseases and similarity measurement between concepts are proposed. They identify the closest concepts of diseases in the context of a corpus. Each document is represented by using the vector space model. A weight scheme is proposed to consider both local content and associations between concepts. Self-Organizing Maps (SOM) are often used as document clustering algorithm. The vector projection and visualization features of SOM enable visualization and analysis of the cluster distribution and relationships on the two dimensional space. The Davies-Bouldin index is used to validate the clusters based on the visualized cluster distributions. The results show that the proposed document clustering framework generates meaningful clusters and can facilitate clustering visualization and information retrieval based on the concepts of diseases.