A Tool for Clustering and Visualising Clusters of Scientific Articles

Student: Grigorev Artur

Educational Programme: Software Engineering (Bachelor)

Year of Graduation: 2017

This paper is dedicated to the task of clustering textual contents of research articles and visualising clusters on a two-dimensional surface. In this paper, we examine methods for document clustering, present results of experimental comparison of those methods and describe various ways for set visualization. The aim of the work was to develop a tool for clustering and visualising clusters of scientific articles built into the web-application Rasearch Supporter currently developed in Institute for system programming of the Russian academy of sciences. We employ chosen during experimental comparison methods paragraph2vec and K-means++ for clustering. Also, algorithm called Bubble Sets was implementer for cluster visualisation.

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