Abstract
The article contains a description of the process and results of the implementation of the k-means algorithm in the analytical platform Loginom for the problem of clustering the regions of Ukraine by the level of investment attractiveness in the field of tourism. The selection of tourism clusters and their ranking is a difficult task in the field of data analysis, as there is no single consolidated indicator of investment attractiveness. The conclusion about the affiliation of a particular region to one of the tourist clusters is determined by a set of indicators of the volume of tourist services for different types of economic activity in the field of tourism. The Loginom system has powerful tools for cluster analysis using EM-Clustering, k-means, g-means and others. The tools of statistical and visual analysis of the obtained results deserve special attention: Table, Statistics, Chart, OLAP-Cube, Cluster Profiles. Clustering has made it possible to identify groups of regions that are actively developing the tourism industry (primarily Kyiv city and Odesa region) and are currently formed for tourism investors. Equally important is the selection of problem regions that have a low level of attractiveness for domestic and foreign tourism. It is noted that Ukraine has a huge potential for the development of the tourism industry. The regions that, according to the results of the cluster analysis, are in the problem group have “world-class tourist pearls”. The Government of Ukraine and local authorities should pay attention to the insufficient level of development of the tourism industry, provide comprehensive support to the regions that are in the problem cluster, and thus increase their level of investment attractiveness.
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Kharlamova, G., Roskladka, A., Roskladka, N., Stavytskyy, A., Zabaldina, Y. (2022). Cluster Analysis of Ukrainian Regions Regarding the Level of Investment Attractiveness in Tourism. In: Ignatenko, O., et al. ICTERI 2021 Workshops. ICTERI 2021. Communications in Computer and Information Science, vol 1635. Springer, Cham. https://doi.org/10.1007/978-3-031-14841-5_10
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