A market basket analysis of the US auto-repair industry

References

• Agrawal, R., Imielinski, T., & Swami, A. (1993, May). Mining association rules between sets of items in large databases. In ACM SIGMOD Intl. Conf. Management of Data.
   Google Scholar
• Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., & Verkamo, A. (1996). Fast discovery of association rules. In  U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy (Eds.),  Advances in knowledge discovery and data mining (pp. 307–328). American Association for Artificial Intelligence, USA.
   Google Scholar
• Agrawal, R., & Srikant, R. (1994). Fast algorithm for mining association rules. In VLDB Conference, pp 487–499.
   Google Scholar
• Aguinis, H., Forcum, L. E., & Joo, H. (2013). Using market basket analysis in management research. Journal of Management, 39(7), 1799–1824. https://doi.org/10.1177/0149206312466147
   Web of Science ®Google Scholar
• Arora, J., Bhalla, N., & Rao, S. (2013, July). A review on association rule mining algorithms. International Journal of Innovative Research in Computer and Communication Engineering, 1(5), 1246–1251. https://doi.org/10.15680/ijircce
   Google Scholar
• Buddhakulsomsiri, J., & Zakarian, A. (2009). Sequential pattern mining algorithm for automotive warranty data. Computers & Industrial Engineering, 57(1), 137–147. https://doi.org/10.1016/j.cie.2008.11.006
   Web of Science ®Google Scholar
• Cavique, L. (2007). A scalable algorithm for the market basket analysis. Journal of Retailing and Consumer Services, 14(6), 400–407. https://doi.org/10.1016/j.jretconser.2007.02.003
   Google Scholar
• Cunningham, S., & Eibe, F. (2001). Market basket analysis of library circulation data.
   Google Scholar
• Doddi, S., Marathe, A., Ravi, S., & Torney, D. (2001). Discovery of association rules in medical data. Medical Informatics and the Internet in Medicine, 26(1), 25–33. https://doi.org/10.1080/14639230010028786
   Google Scholar
• Fransson, M. E., & Fåhraeus, L. (2015). “Finding patterns in vehicle diagnostic trouble codes: A data mining study applying associative classification.”
   Google Scholar
• Gardner, J., Mroueh, J., Jenuwine, N., Weaverdyck, N., Krassenstein, S., Farahi, A., & Koutra, D. (2020, February 23). Driving with data in the motor city: Mining and modeling vehicle fleet maintenance data. http://arxiv.org/pdf/2002.10010.pdf
   Google Scholar
• Hall, S., Osei, A., & McKinley, J. (2018). Using market basket analysis in SAS® Enterprise MinerTM to make student course enrollment recommendations. Proceedings from SAS Global Forum. Paper 2602-2018. https://www.sas.com/content/dam/SAS/support/en/sas-global-forum-proceedings/2018/2602-2018.pdf
   Google Scholar
• Hotz, E., Nakhaeizadeh, G., & Petzsche, B. (1999). WAPS, a data mining support environment for the planning of warranty and goodwill costs in the automotive industry. In: Proceedings of the fifth ACM SIGKDD international conference on knowledge discovery and data mining, San Diego, CA, pp 417–419
   Google Scholar
• John, M., & Shaiba, H. (2019). Apriori-based algorithm for Dubai road accident analysis. Procedia Computer Science, 163, 218–227. https://doi.org/10.1016/j.procs.2019.12.103
   Google Scholar
• Katba, C., (2018, September 14). “Automobile options association rule mining (data mining).” Caroline Katba. http://katba-caroline.com/automobile-options-association-rule-mining-data-mining/
   Google Scholar
• Lakshmi, K. S., & Vadivu, G. (2017). Extracting association rules from medical health records using multi-criteria decision analysis. Procedia Computer Science, 115, 290–295. https://doi.org/10.1016/j.procs.2017.09.137
   Google Scholar
• Lasiritaworn, W. S., Singtorn, N., & Tapeng, P. (2016, October 19–21). Spare parts storage improvement with association rules. Proceedings of the World Congress on Engineering and Computer Science 2016 Vol II WCECS 2016.
   Google Scholar
• Li, C., Ning, D., Gesheng, Z., & Lihua, L. (2019). Association analysis of serial cases based on Apriori algorithm.
   Google Scholar
• Lil, Y., Du, X., & Yang, B. (2015). Application of association analysis and visualization methods in car parts repair. Joint International Mechanical, Electronic and Information Technology Conference (JIMET 2015).
   Google Scholar
• Mavridou, E., Tzovaras, D., Beakiaris, E., Spanidis, P., Gemou, M., & Hassapis, G. (2011). Data mining and intelligent agents for supporting mass customization in the automotive industry. In Chiaberge, M. (Ed.), New Trends and Developments in Automotive Industry. InTech. https://doi.org/10.5772/13135
   Google Scholar
• Mousa, S. R., Bakhit, P. R., Osman, O. A., & Isha, S. (2018). A comparative analysis of tree-based ensemble methods for detecting imminent lane change maneuvers in connected vehicle environments. Transportation Research Record, 2672(42), 268–279. https://doi.org/10.1177/0361198118780204
   Web of Science ®Google Scholar
• Nair, M., & Kayaalp, F. (2019). Performance comparison of association rule algorithms with SPMF on automotive industry data. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 7(3), 1985–2000 .doi: 10.29130/dubited.581931
   Google Scholar
• Pirasteh, P., Nowaczyk, S., Pashami, S., Löwenadler, M., Thunberg, K., Ydreskog, H., & Beck, P. (2019). Interactive feature extraction for diagnostic trouble codes in predictive maintenance: A case study from automotive domain. In Proceedings of the Workshop on Interactive Data Mining (WIDM'19). Association for Computing Machinery, New York, NY, USA. 1–10. https://doi.org/10.1145/3304079.3310288
   Google Scholar
• Sevri, M., Karacan, H., & Akcayol, M. (2017). Crime analysis based on association rules using Apriori algorithm. International Journal of Information and Electronics Engineering, 7(3), 99–102. https://doi.org/10.18178/IJIEE.2017.7.3.669
   Google Scholar
• Tan, P., Steinbach, M., Karpatne, A., & Kumar, V. (2018). Introduction to data mining (2nd edition) (2nd ed.). Pearson.
   Google Scholar
• Višnja, I., & Lalić, N. (2017). Creating a decision-making model using association rules. Applied Artificial Intelligence, 31(5–6), 538–553. https://doi.org/10.1080/08839514.2017.1378301
   Web of Science ®Google Scholar