Fuzzy cluster analysis of indicators for assessing the potential of recreational forest use

- Kryzhanivs’kyi, Evstakhii (orcid.org/0000-0001-6315-1277), Horal, Liliana (orcid.org/0000-0001-6066-5619), Perevozova, Iryna (orcid.org/0000-0002-3878-802X), Shiyko, Vira (orcid.org/0000-0002-2822-0641), Mykytiuk, Nataliia and Berlous, Maria (orcid.org/0000-0003-2856-9832) (2020) Fuzzy cluster analysis of indicators for assessing the potential of recreational forest use Proceedings of the Selected Papers of the Special Edition of International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2-MLPEED 2020) Odessa, Ukraine, July 13-18, 2020, 2731. ISSN 1613-0073

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Abstract

Cluster analysis of the efficiency of the recreational forest use of the region by separate components of the recreational forest use potential is provided in the article. The main stages of the cluster analysis of the recreational forest use level based on the predetermined components were determined. Among the agglomerative methods of cluster analysis, intended for grouping and combining the objects of study, it is common to distinguish the three most common types: the hierarchical method or the method of tree clustering; the K-means Clustering Method and the two-step aggregation method. For the correct selection of clusters, a comparative analysis of several methods was performed: arithmetic mean ranks, hierarchical methods followed by dendrogram construction, K- means method, which refers to reference methods, in which the number of groups is specified by the user. The cluster analysis of forestries by twenty analytical grounds was not proved by analysis of variance, so the re-clustering of certain objects was carried out according to the nine most significant analytical features. As a result, the forestry was clustered into four clusters. The conducted cluster analysis with the use of different methods allows us to state that their combination helps to select reasonable groupings, clearly illustrate the clustering procedure and rank the obtained forestry clusters.

Item Type:	Article
Keywords:	cluster analysis, k-means clustering method, forestry, recreation
Subjects:	Science and knowledge. Organization. Computer science. Information. Documentation. Librarianship. Institutions. Publications > 00 Prolegomena. Fundamentals of knowledge and culture. Propaedeutics > 004 Computer science and technology. Computing. Data processing > 004.9 ІКТ ( Application-oriented computer-based techniques ) > 004.94 Simulation Science and knowledge. Organization. Computer science. Information. Documentation. Librarianship. Institutions. Publications > 3 Social Sciences > 33 Economics. Economic science
Divisions:	Institute for Digitalisation of Education > Joint laboratory with SIHE “Kryvyi Rih National University”
Depositing User:	Сергій Олексійович Семеріков
Date Deposited:	24 Nov 2021 22:50
Last Modified:	25 Nov 2021 07:10
URI:	https://lib.iitta.gov.ua/id/eprint/727236

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