Abstract
Research classification is an important aspect of conducting research projects because it allows researchers to efficiently identify papers that are in line with the latest research in each field and relevant to projects. There are different approaches to the classification of research papers, such as subject-based, methodology-based, text-based, and machine learning-based. Each approach has its advantages and disadvantages, and the choice of classification method depends on the specific research question and available data. The classification of scientific literature helps to better organize and structure the vast amount of information and knowledge generated in scientific research. It enables researchers and other interested parties to access relevant information in a fast and efficient manner. Classification methods allow easier and more accurate extraction of scientific knowledge to be used as a basis for scientific research in each subject area. In this regard, this paper aims to propose a research classification model using data mining methods and techniques. To test the model, we selected scientific articles on digital workplace accessibility for the disabled retrieved from Scopus and Web of Science repositories. We believe that the classification model is universal and can be applied in other scientific fields.
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Funding
The project "Impact of digitalization on innovative approaches in human resources management" is implemented by the University of Economics—Varna, in the period 2022–2025. The authors express their gratitude to the Bulgarian Scientific Research Fund, Ministry of Education and Science of Bulgaria for the support provided in the implementation of the project "Impact of digitalization on innovative approaches in human resources management," Grant No. BG-175467353-2022-04/12-12-2022, contract No. KP-06-H-65/4 – 2022.
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Nacheva, R., Czaplewski, M. & Petrov, P. Data mining model for scientific research classification: the case of digital workplace accessibility. Decision (2024). https://doi.org/10.1007/s40622-024-00378-z
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DOI: https://doi.org/10.1007/s40622-024-00378-z