Abstract
The flooding of online scholarly articles necessitates the automated organization of documents according to their most descriptive attributes. In this paper, an agglomerative similarity measure based on common features associated with research articles, such as number of references, authors, citations, and contents are used for automated clustering of scholarly articles. The agglomerative similarity matrix is based on a combination of citation matrix, author matrix, and the content matrix for feature vector representation. The experiments are performed on agglomerative feature vector derived from wiki20 dataset using different unsupervised learning algorithms such as K-Means, K-medoids, and Fuzzy C-means. The clustering result obtained with modified feature vector is compared to the existing bag of words model using separation and cohesion as performance metrics. The Dunn’s index is used for finding the optimal number of clusters.
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Sisodia, D.S., Choudhary, M., Vandana, T., Rai, R. (2019). Agglomerative Similarity Measure Based Automated Clustering of Scholarly Articles. In: Tanveer, M., Pachori, R. (eds) Machine Intelligence and Signal Analysis. Advances in Intelligent Systems and Computing, vol 748. Springer, Singapore. https://doi.org/10.1007/978-981-13-0923-6_46
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DOI: https://doi.org/10.1007/978-981-13-0923-6_46
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