ISBN: 978-981-11-0008-6 DOI: 10.18178/wcse.2016.06.033
To Be or Not Be Competitive Country: Analysis of Travel and Tourism Competitiveness Index by Multiple Data Mining Techniques
Abstract— Travel and Tourism Competitiveness (TTC) has been raised as an important issue by World
Economic Forum (WEF). The measurements of TTC covering 141 countries around the world provide
information for all stakeholders of each country to enhance tourism competitiveness since tourism
improvement may lead to increasing national growth and wealth. The purpose of this study is to understand
the factors contributing to tourism competiveness and their relationships. The Travel & Tourism
Competitiveness Index (TTCI) of all countries were collected from WEF reports. Then, the dataset were
analysed by three data mining techniques consisting of clustering, classification and association rules mining.
The countries are clustered into 8 segments. Characteristics of each cluster and relationships of TTCI are also
proposed. The revealed results in this paper can be used by the governments and tourism sectors to develop
their strategic plans and management.
Index Terms— travel and tourism competitiveness, data mining, clustering, classification, association rule mining.
Department of Computer Science, Faculty of Science, Kasetsart University, THAILAND
Department of Management, Faculty of Business Administration, Rajamangala University of Technology Thanyaburi, THAILAND
Cite: Anongnart Srivihok, Arunee Intrapairot, "To Be or Not Be Competitive Country: Analysis of Travel and Tourism Competitiveness Index by Multiple Data Mining Techniques," Proceedings of 2016 6th International Workshop on Computer Science and Engineering, pp. 206-213, Tokyo, 17-19 June, 2016.