On K -Means Clustering with IVIF Datasets for Post-COVID-19 Recovery Efforts

Lanndon Ocampo, Joerabell Lourdes Aro, Samantha Shane Evangelista, Fatima Maturan, Egberto Selerio, Nadine May Atibing and Kafferine Yamagishi
Additional contact information
Lanndon Ocampo: Department of Industrial Engineering, Cebu Technological University, Cebu City 6000, Philippines
Joerabell Lourdes Aro: Center for Applied Mathematics and Operations Research, Cebu Technological University, Cebu City 6000, Philippines
Samantha Shane Evangelista: Center for Applied Mathematics and Operations Research, Cebu Technological University, Cebu City 6000, Philippines
Fatima Maturan: Center for Applied Mathematics and Operations Research, Cebu Technological University, Cebu City 6000, Philippines
Egberto Selerio: Center for Applied Mathematics and Operations Research, Cebu Technological University, Cebu City 6000, Philippines
Nadine May Atibing: Center for Applied Mathematics and Operations Research, Cebu Technological University, Cebu City 6000, Philippines
Kafferine Yamagishi: Center for Applied Mathematics and Operations Research, Cebu Technological University, Cebu City 6000, Philippines

Mathematics, 2021, vol. 9, issue 20, 1-30

Abstract: The recovery efforts of the tourism and hospitality sector are compromised by the emergence of COVID-19 variants that can escape vaccines. Thus, maintaining non-pharmaceutical measures amidst massive vaccine rollouts is still relevant. The previous works which categorize tourist sites and restaurants according to the perceived degree of tourists’ and customers’ exposure to COVID-19 are deemed relevant for sectoral recovery. Due to the subjectivity of predetermining categories, along with the failure of capturing vagueness and uncertainty in the evaluation process, this work explores the use k -means clustering with dataset values expressed as interval-valued intuitionistic fuzzy sets. In addition, the proposed method allows for the incorporation of criteria (or attribute) weights into the dataset, often not considered in traditional k -means clustering but relevant in clustering problems with attributes having varying priorities. Two previously reported case studies were analyzed to demonstrate the proposed approach, and comparative and sensitivity analyses were performed. Results show that the priorities of the criteria in evaluating tourist sites remain the same. However, in evaluating restaurants, customers put emphasis on the physical characteristics of the restaurants. The proposed approach assigns 12, 15, and eight sites to the “low exposure”, “moderate exposure”, and “high exposure” cluster, respectively, each with distinct characteristics. On the other hand, 16 restaurants are assigned “low exposure”, 16 to “moderate exposure”, and eight to “high exposure” clusters, also with distinct characteristics. The characteristics described in the clusters offer meaningful insights for sectoral recovery efforts. Findings also show that the proposed approach is robust to small parameter changes. Although idiosyncrasies exist in the results of both case studies, considering the characteristics of the resulting clusters, tourists or customers could evaluate any tourist site or restaurant according to their perceived exposure to COVID-19.

Keywords: COVID-19; tourism industry; hospitality sector; interval-valued intuitionistic fuzzy set; k-means clustering (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/9/20/2639/pdf (application/pdf)
https://www.mdpi.com/2227-7390/9/20/2639/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:9:y:2021:i:20:p:2639-:d:660009

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().