Performance Comparison of K-Means and DBSCAN Methods for Airline Customer Segmentation

Year 2022, , 158 - 165, 01.10.2022

Kevser Şahinbaş

https://doi.org/10.34248/bsengineering.1170943

Cited By: 1

Abstract

Organizations are now fully embracing ideas such as customer success, customer loyalty, customer experience management and customer satisfaction. The application of these concepts must be based on three pillars of technology, process and people, to ensure that the organization ultimately has satisfied, loyal and successful customers. In today's competitive environment, as in all sectors, gaining great services in the aviation industry can provide a competitive advantage. With this study, it is aimed to help aviation companies to know how their services should meet the needs of customers and to obtain passenger satisfaction. Customer segmentation is widely used, which groups objects according to the similarity difference on each object and provides a high level of homogeneity in the same cluster or a high level of heterogeneity between each group. The aim of this study is to examine airline passenger satisfaction by using data mining methods including K-Means and Density-based spatial clustering of applications with noise (DBSCAN) clustering algorithms to reveal the service quality importance for customer satisfaction. K-Means algorithm achieved slightly better results than DBSCAN algorithm with a Silhouette value of 0.1450671.

Keywords

Clustering, Customer segmentation, K-means, DBSCAN, Data mining, Data management

References

• Ajin VW, Kumar LD. 2016. Big data and clustering algorithms. International conference on research advances in integrated navigation systems (RAINS) IEEE, 6-7 May 2016, Bangalore, India, pp: 1-5.
• Ariffin Mohd IA, Yajid SA, Johar MGM. 2020. Consumer preferences of airline choice: A comparison of Air Asia and Malaysia Airlines System. Syst Rev Pharm, 11(1): 817-826.
• Archana R, Subha MV. 2012. A study on service quality and passenger satisfaction on Indian airlines, Int J Multidis Res, 2(2): 50-63.
• Bustamam A, Tasman H, Yuniarti N, Mursidah I. 2017. Application of K-means clustering algorithm in grouping the DNA sequences of hepatitis B virus (HBV). AIP Conf Proc, 1862(1): 030134.
• Caliński T, Harabasz J. 1974. A dendrite method for cluster analysis. Commun Stat Theo Meth, 3(1): 1-27.
• Cassisi C, Ferro A, Giugno R, Pigola G, Pulvirenti, A. 2013. Enhancing density-based clustering: parameter reduction and outlier detection. Inf Syst, 38(3): 317-330.
• Chang YH, Yeh CH. 2002. A survey analysis of service quality for domestic airlines. European J Oper Res, 139(1): 166-177. DOI: 10.1016/S0377-2217(01)00148-5.
• Chen Z, Li YF. 2011. Anomaly detection based on enhanced DBScan algorithm. Procedia Eng, 15: 178-182.
• Cui H, Wu W, Zhang Z, Han F, Liu Z. 2021. Clustering and application of grain temperature statistical parameters based on the DBSCAN algorithm. J Stored Prod Res, 93: 101819.
• Deveci M, Demirel NÇ. 2018. A survey of the literature on airline crew scheduling. Eng App Artif Intel, 74: 54-69.
• Ester M, Kriegel HP, Sander J, Xu X. 1996. A density based algorithm for discovering clusters in large spatial databases. Int. Conference of Knowledge Discovery and Data Mining (KDD’96), Portland, USA, pp: 226-231.
• Davies DL, Bouldin DW. 1979. A cluster separation measure. IEEE Transact Pattern Analysis Machine Intel, 2: 224-227.
• Du Z. 2020. Energy analysis of Internet of things data mining algorithm for smart green communication networks. Comp Commun, 152: 223-231.
• Fahim A. 2021. K and starting means for k-means algorithm. J Comput Sci, 55: 101445.
• Farooq MS, Radovic-Markovic M. 2016. Modeling entrepreneurial education and entrepreneurial skills as antecedents of intention towards entrepreneurial behaviour in single mothers: a PLS-SEM approach. ETCTFP, 2016: 198-216.
• Goharnejad H, Shamsai A, Zakeri Niri M. 2019. Pridiction of sea level rise in the south of iran coastline: evaluation of climate change impacts. Water Res Eng, 12(42): 1-17.
• Jiang H, Zhang Y. 2016. An investigation of service quality, customer satisfaction and loyalty in China’s airline market. J Air Trans Manag, 57: 80-88.
• Han J, Pei J, Kamber M. 2011. Data mining: concepts and techniques. Elsevier, New York, US, pp: 703.
• Hao F, Zhang J, Duan Z, Zhao L, Guo L, Park DS. 2020. Urban area function zoning based on user relationships in location-based social networks. IEEE Access, 8: 23487-23495.
• Hanafi N, Saadatfar H. 2022. A fast DBSCAN algorithm for big data based on efficient density calculation. Expert Sys App, 203: 117501.
• Hartigan JA, Wong MA. 1979. Algorithm AS 136: A k-means clustering algorithm. J Royal Stat Soc Series c, 28(1): 100-108.
• Jou RC, Lam SH, Hensher DA, Chen CC, Kuo CW. 2008. The effect of service quality and price on international airline competition. Transport Res Part E, 44(4): 580-592.
• Jahirabadkar S, Kulkarni P. 2014. Algorithm to determine ε-distance parameter in density based clustering. Expert Sys App, 41(6): 2939-2946.
• Kaufman L, Rosseeauw PJ. 1990. Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley and Sons Inc., New York, US, pp: 335.
• Leon S, Martín JC. 2020. A fuzzy segmentation analysis of airline passengers in the US based on service satisfaction. Res Transport Busin Manag, 37: 100550.
• Ketchen DJ, Shook CL. 1996. The application of cluster analysis in strategic management research: an analysis and critique. Strat Manag J, 17(6): 441-458.
• Masood MA, Khan MNA. 2015. Clustering techniques in bioinformatics. IJ Modern Educ Comp Sci, 1: 38-46.
• Munusamy J, Chelliah S, Pandian S. 2011. Customer satisfaction delivery in airline industry in Malaysia: a case of low cost carrier. Australian J Basic App Sci, 5(11): 718-723.
• Noviantoro T, Huang JP. 2022. Investigating airline passenger satisfaction: Data mining method. Res Transport Busin Manag, 43: 100726.
• Majhi SK, Biswal S. 2018. Optimal cluster analysis using hybrid K-Means and Ant Lion Optimizer. Karbala Int J Modern Sci, 4(4): 347-360.
• Mahesh B. 2020. Machine learning algorithms-a review. Int J Sci Res, 9: 381-386.
• Straka M, Buzna LU. 2019. Clustering algorithms applied to usage related segments of electric vehicle charging stations. Transport Res Proc, 40: 1576-1582.
• Teichert T, Shehu E, von Wartburg I. 2008. Customer segmentation revisited: The case of the airline industry. Transport Res Part A, 42(1): 227-242.
• Rousseeuw PJ. 1987. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis, J Comput App Math, 20: 53-65.
• Saeed MM, Al Aghbari Z, Alsharidah M. 2020. Big data clustering techniques based on spark: a literature review. Peer J Comp Sci, 6: e321.
• Santhanam T, Padmavathi MS. 2015. Application of K-means and genetic algorithms for dimension reduction by integrating SVM for diabetes diagnosis. Procedia Comp Sci, 47: 76-83.
• Taylor R. 1990. Interpretation of the correlation coefficient: a basic review. J Diag Medic Sonograp, 6(1): 35-39.
• Yelmen İ, Üstebay S, Zontul M. 2020. Customer segmentation based on self-organizing maps: a case study on airline passengers. J Aeronautics Space Technol, 13(2): 227-233.