In the real world, there are objective relationships and connections between objects. And that is why, along with the definition of concepts, their division and classification play a special role. When studying a concept, if we consider it to explore reality, we always focus on the distribution of objects that are thought of in the concept, i.e., willingly or unwillingly, we reveal its volume. The division of concepts is the division of the books of concepts.
When dividing and classifying, you always need to correctly define the concept that needs to be classified because “selection is a critical preprocess for constructing” (Li et al., 2020, par.1). The division is a logical action through which the concept’s scope under consideration is distributed among a number of subsets using the chosen basis. At the same time, division reveals the scope of the concept by highlighting the possible types of elements included in it.
Classification is the distribution of objects into groups (classes), where each class has a constant, definite place. According to Tharwat “the assessment method is a key factor in evaluating the classification performance and guiding the classifier modeling” (Tharwat, 2020, p.168). The classification is obtained as a result of a preliminary sequential multistage division of the concept. This is a kind of system of mereological and taxonomic divisions of concepts.
I would always use division and classification together as two related concepts. The classification makes it possible to consider the diversity in a particular system, to highlight the types to reveal the connections between the classified objects. Initially, it is crucial to separate concepts for a better understanding of the individual parts. Then, I would classify the necessary concepts in order to organize the received data. Thus, division and classification are integral parts of one whole, which will help consider the concept in full.
Li, H., He, F., Liang, Y., Quan, Q. (2020). A dividing-based many-objective evolutionary algorithm for large-scale feature selection. Soft Computing, 24 (6851).
Tharwat, A. (2020). Classification assessment methods. New England Journal of Entrepreneurship, 17(1), pp. 168-192.