Clusterization by level of financial management of innovation–oriented agricultural enterprises in the conditions of activation of logistics and foreign economic activities, digitalization and change management

Relevance of the research topic. The study of the issue of clustering by the level of financial management of innovatively oriented agricultural enterprises in the conditions of the activation of logistics and foreign economic activities, digitalization and change management is conditioned by the lack of a unified approach to the implementation of the algorithm of this process. Formulation of the problem. One of the main tasks of cluster analysis is to depict primary information in a condensed form without extreme loss of information. Cluster analysis assumes the selection of compact, separated groups of objects and carries out a "natural" division of the population into areas of clustering of objects. It is the clustering according to the level of financial management of innovatively oriented agricultural enterprises in the context of the activation of logistics and foreign economic activity, digitalization and change management that determines the relevance of the research topic. Setting the purpose and objectives of the study – to investigate the clustering by the level of financial management of innovatively oriented agricultural enterprises in the conditions of intensification рівof logistics and foreign economic activities, digitalization and change management. Research method or methodology. The article uses the following methods: hierarchical clustering, economic–mathematical, economic–statistical, graphic, monographic, analysis and synthesis, systematization. Presentation of the main material (research results). Cluster analysis will allow identification of innovatively oriented agricultural enterprises according to the level of compliance with the market value of their business depending on the calculated values of the cost components into two groups: undervalued and overvalued enterprises. The key point of cluster analysis is the selection of the distance between objects. The final version of dividing objects into classes depends on it. The choice of distances is determined by the structure of the feature space. Field of application of results. The results of the research can be used in practical activities to improve clustering by the level of financial management of innovatively oriented agricultural enterprises in the conditions of intensification of logistics and foreign economic activities, digitalization and change management. Conclusions on the article. It has been proven that the main idea of hierarchical algorithms is as follows: first, each object is considered a separate cluster. The two closest objects (clusters) are combined and form a new cluster. In the process of division, new clusters are formed until the stopping rule is met. Thus, non–hierarchical clustering by the level of financial management of innovatively oriented agricultural enterprises in the conditions of intensification of logistics and foreign economic activities, digitalization and change management consists in dividing the data set into a certain number of separate clusters