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Journals / Current Perspectives in Social Sciences / 2021 / Cilt: 25 - Sayı: 3

Association Analysis Between Airline Destinations

Ali Rıza İNCE
Pages
1260–1289
Publication Date
2021-09-28
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

It is presented as a suggestion that airlines can benefit from these rules in their destination planning.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Abstract: This study aims to contribute to the strategic decisions of airline companies in determining of their destinations.
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Key Findings & Data:Using the data of 617 airlines, it was tried to investigate whether there are rules of association between the companies' destinations.
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Conclusion & Impact:It is presented as a suggestion that airlines can benefit from these rules in their destination planning.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Stratejik Yönetim · Destinasyon Planlama · Veri Madenciliği

Abstract

Abstract: This study aims to contribute to the strategic decisions of airline companies in determining of their destinations. Using the data of 617 airlines, it was tried to investigate whether there are rules of association between the companies' destinations. Through the analysis made with two different data sets, the rules of association were sought considering both all the destinations and the foreign destinations of the companies. In the analysis of all destinations, 197 rules could be produced with 90% and above confidence level. In the analysis between the companies' foreign destinations, 200 rules with 100% confidence level could be produced. It is presented as a suggestion that airlines can benefit from these rules in their destination planning.

Özet

Öz: Bu çalışma ile havayolu şirketlerinin varış noktalarını belirleme konusundaki stratejik kararlarına katkı sağlamak amaçlanmıştır. 617 havayolu şirketine ait veriler kullanılarak, şirketlerin varış noktaları arasında birliktelik kurallarının olup olmadığı araştırılmaya çalışılmıştır. İki farklı veri seti ile yapılan analizlerle hem tüm varış noktaları, hem de şirketlerin yurt dışı varış noktaları dikkate alınarak birliktelik kuralları aranmıştır. Tüm varış noktaları ilgili analizde %90 ve üzeri güven seviyesinde 197 kural üretilebilmiştir. Şirketlerin yurt dışı varış noktaları arasındaki analizde ise %100 güven seviyende 200 kural üretilebilmiştir. Havayolu şirketlerinin varış noktaları planlamalarında, bu kurallardan yararlanabilecekleri, bir öneri olarak sunulmuştur.

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Cite This Article

İNCE, A. R. (2021). Association Analysis Between Airline Destinations. Current Perspectives in Social Sciences, Cilt: 25 - Sayı: 3, 1260–1289. https://ajindex.com/articles/415630

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References (11)

  1. Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. In Proceedings of the 20th international conference on very large data bases, 1994
  2. Santiago, Chile. Citeseer (pp.487–499).
  3. Baker, R. S.J.d. (2010),
  4. Data Mining for Education, To appear in McGaw, B., Peterson, P., Baker, E. (Eds.) International Encyclopedia of Education (3rd edition). Oxford, UK: Elsevier. Barrett, S. D. (2004). How do the demands for airport services differ between full-service carriers and low-cost carriers?. Journal of air transport management, 10(1), 33-39.
  5. Belobaba, P., Odoni, A., & Barnhart, C. (Eds.). (2015). The global airline industry. John Wiley & Sons. Belton, V., & Stewart, T. (2002). Multiple criteria decision analysis: an integrated approach. Springer Science & Business Media. Bieger, T., & Wittmer, A. (2006). Air transport and tourism—Perspectives and challenges for destinations, airlines and governments. Journal of air transport management, 12(1), 40-46.
  6. Bose, I., Chun, L. A.,Yue, L. V. W., Ines, L. H. W. and Helen, W. O. L. (2009), “Business Data Warehouse: The Case of Wal-Mart”, Data Mining Applications for Empowering Knowledge Societies, Ed. Hakikur Rahman, Information Science Reference, pp.189-198
  7. Calder, S. (2003). No Frills: The truth behind the low-cost revolution in the skies. Virgin Pub. Chang, Y. C., & Lee, N. (2010). A Multi-Objective Goal Programming airport selection model for low-cost carriers’ networks. Transportation Research Part E: Logistics and Transportation Review, 46(5), 709-718.
  8. Chang, Y. C., Lee, W. H., & Wu, C. H. (2019). Airline new route selection using compromise programming-The case of Taiwan-Europe. Journal of Air Transport Management, 76, 10-20.
  9. Chang, Y. C., Woon, H. K., Yen, J. R., & Hsu, C. J. (2017). A destination selection model for long haul routes–The case of Taiwan-US. Journal of Air Transport Management, 64, 60-67.
  10. Deveci, M., Demirel, N. Ç., & Ahmetoğlu, E. (2017). Airline new route selection based on interval type-2 fuzzy MCDM: A case study of new route between Turkey-North American region destinations. Journal of Air Transport Management, 59, 83-99.
  11. Doganis, R. (2006). The airline business. Psychology Press. Dožić, S. (2019). Multi-criteria decision making methods: Application in the aviation industry. Journal of Air Transport Management, 79, 101683. Fayyad, Usama, Gregory Piatetsky-Shapiro, and Padhraic Smyth (1996). From data mining to knowledge discovery in databases, AI magazine 17.3:37-54. Francis,