Abstract
Biomedical research is progressing remarkably and there arises a necessity for identifying most interested sub-research discipline in biomedicine. This sounds similar to identifying the popular research sub-filed which is growing in fast pace under biomedical research. Application of topic models upon research articles derives to better clustering algorithms which reveal interesting insights to the underlying research problem. This paper proposes a new clustering algorithm which is a fusion of GSDMM and HDP. The resulting scientific article clusters are compared with K-means clustering, which reveals interesting results.
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Mahalakshmi, G.S., MuthuSelvi, G., Sendhilkumar, S. (2020). Gibbs Sampled Hierarchical Dirichlet Mixture Model Based Approach for Clustering Scientific Articles. In: Elçi, A., Sa, P., Modi, C., Olague, G., Sahoo, M., Bakshi, S. (eds) Smart Computing Paradigms: New Progresses and Challenges. Advances in Intelligent Systems and Computing, vol 766. Springer, Singapore. https://doi.org/10.1007/978-981-13-9683-0_18
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DOI: https://doi.org/10.1007/978-981-13-9683-0_18
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