On <i>K</i>-Means Clustering with IVIF Datasets for Post-COVID-19 Recovery Efforts
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 restaurant...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-10-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/9/20/2639 |
| _version_ | 1797513968299802624 |
|---|---|
| author | Lanndon Ocampo Joerabell Lourdes Aro Samantha Shane Evangelista Fatima Maturan Egberto Selerio Nadine May Atibing Kafferine Yamagishi |
| author_facet | Lanndon Ocampo Joerabell Lourdes Aro Samantha Shane Evangelista Fatima Maturan Egberto Selerio Nadine May Atibing Kafferine Yamagishi |
| author_sort | Lanndon Ocampo |
| collection | DOAJ |
| description | 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>k</mi></semantics></math></inline-formula>-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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>k</mi></semantics></math></inline-formula>-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. |
| first_indexed | 2024-03-10T06:25:00Z |
| format | Article |
| id | doaj.art-78f168b338834ccf83910390a4ee0ba5 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T06:25:00Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-78f168b338834ccf83910390a4ee0ba52023-11-22T19:03:05ZengMDPI AGMathematics2227-73902021-10-01920263910.3390/math9202639On <i>K</i>-Means Clustering with IVIF Datasets for Post-COVID-19 Recovery EffortsLanndon Ocampo0Joerabell Lourdes Aro1Samantha Shane Evangelista2Fatima Maturan3Egberto Selerio4Nadine May Atibing5Kafferine Yamagishi6Department of Industrial Engineering, Cebu Technological University, Cebu City 6000, PhilippinesCenter for Applied Mathematics and Operations Research, Cebu Technological University, Cebu City 6000, PhilippinesCenter for Applied Mathematics and Operations Research, Cebu Technological University, Cebu City 6000, PhilippinesCenter for Applied Mathematics and Operations Research, Cebu Technological University, Cebu City 6000, PhilippinesCenter for Applied Mathematics and Operations Research, Cebu Technological University, Cebu City 6000, PhilippinesCenter for Applied Mathematics and Operations Research, Cebu Technological University, Cebu City 6000, PhilippinesCenter for Applied Mathematics and Operations Research, Cebu Technological University, Cebu City 6000, PhilippinesThe 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>k</mi></semantics></math></inline-formula>-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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>k</mi></semantics></math></inline-formula>-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.https://www.mdpi.com/2227-7390/9/20/2639COVID-19tourism industryhospitality sectorinterval-valued intuitionistic fuzzy setk-means clustering |
| spellingShingle | Lanndon Ocampo Joerabell Lourdes Aro Samantha Shane Evangelista Fatima Maturan Egberto Selerio Nadine May Atibing Kafferine Yamagishi On <i>K</i>-Means Clustering with IVIF Datasets for Post-COVID-19 Recovery Efforts Mathematics COVID-19 tourism industry hospitality sector interval-valued intuitionistic fuzzy set k-means clustering |
| title | On <i>K</i>-Means Clustering with IVIF Datasets for Post-COVID-19 Recovery Efforts |
| title_full | On <i>K</i>-Means Clustering with IVIF Datasets for Post-COVID-19 Recovery Efforts |
| title_fullStr | On <i>K</i>-Means Clustering with IVIF Datasets for Post-COVID-19 Recovery Efforts |
| title_full_unstemmed | On <i>K</i>-Means Clustering with IVIF Datasets for Post-COVID-19 Recovery Efforts |
| title_short | On <i>K</i>-Means Clustering with IVIF Datasets for Post-COVID-19 Recovery Efforts |
| title_sort | on i k i means clustering with ivif datasets for post covid 19 recovery efforts |
| topic | COVID-19 tourism industry hospitality sector interval-valued intuitionistic fuzzy set k-means clustering |
| url | https://www.mdpi.com/2227-7390/9/20/2639 |
| work_keys_str_mv | AT lanndonocampo onikimeansclusteringwithivifdatasetsforpostcovid19recoveryefforts AT joerabelllourdesaro onikimeansclusteringwithivifdatasetsforpostcovid19recoveryefforts AT samanthashaneevangelista onikimeansclusteringwithivifdatasetsforpostcovid19recoveryefforts AT fatimamaturan onikimeansclusteringwithivifdatasetsforpostcovid19recoveryefforts AT egbertoselerio onikimeansclusteringwithivifdatasetsforpostcovid19recoveryefforts AT nadinemayatibing onikimeansclusteringwithivifdatasetsforpostcovid19recoveryefforts AT kafferineyamagishi onikimeansclusteringwithivifdatasetsforpostcovid19recoveryefforts |