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

• Published in: Mathematics (Basel) 2021-10, Vol.9 (20), p.2639
• Main Authors: Ocampo, Lanndon, Aro, Joerabell Lourdes, Evangelista, Samantha Shane, Maturan, Fatima, Selerio, Egberto, Atibing, Nadine May, Yamagishi, Kafferine
• Format: Article
• Language: English
• Subjects: Algorithms; Bias; Cluster analysis; Clustering; Coronaviruses; COVID-19; Criteria; Customers; Datasets; Decision making; Evaluation; Exposure; Fuzzy sets; hospitality sector; interval-valued intuitionistic fuzzy set; k-means clustering; Literature reviews; Mathematics; Pandemics; Parameter robustness; Physical properties; Preferences; Priorities; Recovery; Restaurants; Tourism; tourism industry; Tourist attractions; Vaccines; Vector quantization
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math9202639

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.