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Article · Lobachevskii Journal of Mathematics · December 1, 2022

Analysis of the Cluster Structure of Collections of Mathematical Papers with Different UDC Codes

Olga Avenirovna Nevzorova, B. T. Gizatullin

Bulat Gizatullin
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Abstract

Clustering is the task of dividing data objects into groups of similar objects. The influence of the specifics of the texts of scientific articles of one subject area for clustering problems has been little studied at present. This article is devoted to the problem of clustering collections of mathematical papers that have different Universal Decimal Classification (UDC) codes. The study was carried out on the collection of mathematical papers published in the journal ‘‘Izvestiya VUZov. Matematika’’ for 10 years. The size of this collection is 623 original papers with different UDC codes. The collection contains subcollections of papers that have the same UDC code. The objective of our research is to analyze the cluster structure of representative subcollections of papers with the same UDC code, which will allow us to evaluate various parameters of the constructed clusters in the future. We have performed the standard pre-processing (tokenization, lemmatization) of Russian math texts. To vectorize text data, we used a method based on $$t\!f$$ - $$idf$$ . All vectors were normalized using the L2 norm. To identify optimal hyperparameters and check the quality of clustering, we have used internal efficiency measures such as Silhouette coefficient, Calinski–Harabaz index. The optimal hyperparameters were selected for each method, and then the results of clustering were compared. As a result, we have selected the optimal methods of clustering math papers.

Advanced Clustering Algorithms ResearchText and Document Classification TechnologiesComputational and Text Analysis Methodsdocument clusteringUniversal Decimal ClassificationTF-IDF vectorization

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