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

Author

Listed:
  • Lanndon Ocampo

    (Department of Industrial Engineering, Cebu Technological University, Cebu City 6000, Philippines
    Center for Applied Mathematics and Operations Research, 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
    Department of Tourism Management, Cebu Technological University, Cebu City 6000, Philippines)

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.

Suggested Citation

  • Lanndon Ocampo & Joerabell Lourdes Aro & Samantha Shane Evangelista & Fatima Maturan & Egberto Selerio & Nadine May Atibing & Kafferine Yamagishi, 2021. "On K -Means Clustering with IVIF Datasets for Post-COVID-19 Recovery Efforts," Mathematics, MDPI, vol. 9(20), pages 1-30, October.
  • Handle: RePEc:gam:jmathe:v:9:y:2021:i:20:p:2639-:d:660009
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    References listed on IDEAS

    as
    1. Sarah Dryhurst & Claudia R. Schneider & John Kerr & Alexandra L. J. Freeman & Gabriel Recchia & Anne Marthe van der Bles & David Spiegelhalter & Sander van der Linden, 2020. "Risk perceptions of COVID-19 around the world," Journal of Risk Research, Taylor & Francis Journals, vol. 23(7-8), pages 994-1006, August.
    2. Hu, Kejia & Tan, Qian & Zhang, Tianyuan & Wang, Shuping, 2020. "Assessing technology portfolios of clean energy-driven desalination-irrigation systems with interval-valued intuitionistic fuzzy sets," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    3. Dogan Gursoy & Ali Selcuk Can & Nigel Williams & Yuksel Ekinci, 2021. "Evolving impacts of COVID-19 vaccination intentions on travel intentions," The Service Industries Journal, Taylor & Francis Journals, vol. 41(11-12), pages 719-733, September.
    4. Lisa A Prosser & Tara A Lavelle & Anthony E Fiore & Carolyn B Bridges & Carrie Reed & Seema Jain & Kelly M Dunham & Martin I Meltzer, 2011. "Cost-Effectiveness of 2009 Pandemic Influenza A(H1N1) Vaccination in the United States," PLOS ONE, Public Library of Science, vol. 6(7), pages 1-11, July.
    5. Ocampo, Lanndon & Yamagishi, Kafferine, 2020. "Modeling the lockdown relaxation protocols of the Philippine government in response to the COVID-19 pandemic: An intuitionistic fuzzy DEMATEL analysis," Socio-Economic Planning Sciences, Elsevier, vol. 72(C).
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