C&SA | Contents

Volume 57 >>> № 3 MAY — JUNE 2021

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UDC 519.237.8

N.E. Kondruk¹, M.M. Malyar²

¹ Uzhhorod National University, Uzhhorod, Ukraine

natalia.kondruk@uzhnu.edu.ua

² Uzhhorod National University, Uzhhorod, Ukraine

mykola.malyar@uzhnu.edu.ua

ANALYSIS OF CLUSTER STRUCTURES BY DIFFERENT SIMILARITY MEASURES

Abstract. The cluster analysis formations used in practical tasks is presented. In various studies, data segmentation is usually performed with only one type of clusters. It is proposed to carry out clustering by various similarity measures to the same investigated data and to identify different types of relationships between them. This allows for a more complete, versatile, and systematic analysis of the formed segments in applied problems. This approach is verified using a practical problem of analyzing demographic processes in some European countries.

Keywords: clustering, cluster analysis, cluster interpretation, demographic processes.

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REFERENCES

Hulianytskyi L.F., Riasna I.I. Automatic classification method based on a fuzzy similarity relation. Cybernetics and Systems Analysis. 2016. Vol. 52, N 1. P. 30–37. https://doi.org/10.1007/ s10559-016-9796-3.

Lascu D.-N., Manrai L.A., Manrai A.K., Gan A. A cluster analysis of tourist attractions in Spain: Natural and cultural traits and implications for global tourism. European Journal of Management and Business Economics. 2018. Vol. 27, N 3. P. 218–230. https://doi.org/10.1108/EJMBE-08-2017-0008.

Sanders I., Short C.E., Bogomolova S., Stanford T., Plotnikoff R., Vandelanotte C., Olds T., Edney S., Ryan J., Curtis R.G., Maher C. Characteristics of adopters of an online social networking physical activity mobile phone app: Cluster analysis. JMIR Mhealth Uhealth. 2019. Vol. 7, N 6. P. 1–11. https://doi.org/10.2196/12484.

Morton C., Anable J., Nelson J.D. Consumer structure in the emerging market for electric vehicles: Identifying market segments using cluster analysis. International Journal of Sustainable Transportation. 2017. Vol. 11, N 6. P. 443–459. https://doi.org/10.1080/15568318.2016.1266533.

Kondruk N. Clustering method based on fuzzy binary relation. Eastern-European Journal of Enterprise Technologies. 2017. Vol. 2, N 4. P. 10–16. https://doi.org/10.15587/1729-4061.2017.94961.

Haben S., Singleton C., Grindrod P. Analysis and clustering of residential customers energy behavioral demand using smart meter data. IEEE Trans. on Smart Grid. 2015. Vol. 7, N 1. P. 136–144. https://doi.org/10.1109/TSG.2015.2409786.

Geng D., Xia X., Fu X. A knowledge discovery method for characteristics extraction of power consumption based on cluster analysis. Chinese Automation Congress (22–24 Nov 2019, Hangzhou, China). Hangzhou, China, 2019. P. 1288–1292. https://doi.org/10.1109/CAC48633.2019.8996438.

Leech R.M., McNaughton S.A., Timperio A. The clustering of diet, physical activity and sedentary behavior in children and adolescents: a review. International Journal of Behavioral Nutrition and Physical Activity. 2014. Vol. 11, N 4. P. 1–9. https://doi.org/10.1186/1479-5868-11-4.

Wang G., Zhang X., Tang S., Zheng H., Zhao B. Unsupervised clickstream clustering for user behavior analysis. CHI Conf. on Human Factors in Computing Systems (6–12 May 2016, New York). New York, 2016. P. 225–236. https://doi.org/10.1145/2858036.2858107.

Zhang D., Ortiz M.A., Bluyssen P.M. Clustering of Dutch school children based on their preferences and needs of the IEQ in classrooms. Building and Environment. 2019. Vol. 147. P. 258–266. https://doi.org/10.1016/j.buildenv.2018.10.014.

Kim D.H., Bluyssen P.M. Clustering of office workers from the OFFICAIR study in The Netherlands based on self-reported health and comfort. Building and Environment. 2020. Vol. 176. P. 1–19. https://doi.org/10.1016/j.buildenv.2020.106860.

Кondruk N.E. Use of length-based similarity measure in clustering problems. Radio Electronics. Computer Science. Control. 2018. N 3. P. 98–105. https://doi.org/10.15588/1607-3274-2018-3-11.

Eurostat. European Statistical System. URL: https://ec.europa.eu/.

Кondruk N.E. A comparative study of cluster validity indices. Radio Electronics. Computer Science. Control. 2019. N 4. P. 59–67. https://doi.org/10.15588/1607-3274-2019-4-6.

Malyar М.М., Кondruk N.E. Structuring the criterion space by the angular measure of similarity. Nauk. visn. Uzhhorod. un-tu. Series "Mathematics and Informatics”. 2020. Iss. N 1 (36). P. 85–91. https://doi.org/10.24144/2616-7700.2020.1(36).85-91.