Data Mining for Research Purposes
DOI:
https://doi.org/10.15407/sofs2025.02.027Keywords:
data mining, empirical regularity, training sample, feature space, data design.Abstract
The article considers the possibility of using Data Mining (DM) methods in a scientific study aimed at cognition, which requires an answer to the question ‘why?’, as well as the connection of DM methods with general questions of cognition. The research methodology is at the intersection of mathematics, statistics, computer science, and management theory. Along with the use of general scientific methods of cognition (generalization, systematization, abstraction, induction and deduction, analysis and synthesis, analogy, comparison, formalization, modelling, classification, categorization), special methods of analysis (logical, structural, functional), descriptive research method, and interpretive methodology are applied. The relationship of DM methods with the general theory of cognition is clarified. It is proved that DM methods are a new, human-machine methodology of empirical cognition. The authors point out the limitations of these methods: the results of their use are empirical regularities (ERs), i.e. probable (rather than exact) knowledge, on the basis of which instructions or decision-making rules are formulated, but remain at the first, lowest possible, empirical level of knowledge, which is typical for business. However, the main goal of scientific research is to obtain truly new knowledge that can be translated into innovations and provide a higher-order competitive advantage. ESMs can serve as ‘blanks’ for formulating, testing and selecting hypotheses in order to further deepen the knowledge of the subject area and obtain more reliable knowledge that brings the researcher closer to understanding. And if understanding is gained, it means that something has been found that underlies the knowledge gained, and this can become a new method of solving the tasks at hand. Knowledge is information, and understanding is the ability to draw conclusions. Today, this is the limit of applicability of all DM tools: they provide knowledge in the form of empirical and working hypotheses, but do not provide understanding, without which it is impossible to move to the next level of knowledge, the scientific method.
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