Abstract:

In a broad sense, the author’s study was preconditioned by the problem of arranging the efficient system for positioning of the enterprises of venture entrepreneurship acceleration and incubation that today is quite a controversial aspect. At the same time, for today there are no studies and publications related to the identification of approaches to the positioning of clusters of the enterprise innovative activity acceleration that will make it possible to find the most efficient approaches to acceleration. Therefore, this paper is aimed at identifying the basic approaches in the positioning of startup acceleration and incubation institutes. Methodology. The study is based on the algorithms of cluster analysis. Having identified the key parameters of the analysed institutions, using the “Statistics7” program package, we have combined the input data to address the challenge of cluster analysis, consisting in classification breakdown, which meets the criterion of optimality. Proceeding from the research target, the rule “Single Linkage” was chosen as the key function. According to this rule, there are two objects, being the closest to each other. At the next step, the object, which has the maximum degree of similarity with one of the cluster objects, joins them. This method is also called the method of the closest neighbour as the distance between two clusters is defined as the distance between the closest two objects in different clusters. 1 - Pearson is chosen as a degree of distance as far as the given data cannot be presented as points in k-dimensional space. Results. The first part of this research contains a justification of the positioning indicators among the analysed startup accelerators, which were chosen for cluster analysis according to functioning models. In the second part, we consider the distinction of the functioning models of the obtained clusters and the features, typical for every enterprise, which has been involved in the study. Proceeding from the calculation of the configuration of distances of cluster formations using the method of k-means while approaching the process of startup acceleration and incubation, we have identified the aspect, which is of primary importance for the formation of resultative startup-accelerator. Practical implications. The obtained results make it possible to optimize the resources in the process of creation and positioning of startup accelerators and incubators. Value/originality. Based on the developed configuration of the distances of cluster formations using the method of к-means in approached-to-startup acceleration and incubation process, we have found out the best practices in the area of venture business acceleration.

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