Mining and Modeling Both Association Patterns and Determinants for the Attendance of Performing Arts Activities

References

• Agrawal, R. S., & Srikant, R. (1994). Fast algorithms for mining association rules. In Proceedings of the 20th International Conference on Very Large Data Bases VLDB, Santiago, Chile, (Vol. 1215, pp. 487–499).
   Google Scholar
• Agresti, A. (2012). Categorical data analysis. John Wiley & Sons.
   Google Scholar
• Andreasen, A. R., & Belk, R. W. (1980). Predictors of attendance at the performing arts. Journal of Consumer Research, 7(2), 112–120. https://doi.org/10.1086/208800
   Web of Science ®Google Scholar
• Andrejevic, M., Hearn, A., & Kennedy, H. (2015). Cultural studies of data mining: Introduction. European Journal of Cultural Studies, 18(4–5), 379–394. https://doi.org/10.1177/1367549415577395
   Web of Science ®Google Scholar
• Aqra, I., Abdul Ghani, N., Maple, C., Machado, J., & Sohrabi Safa, N. (2019). Incremental algorithm for association rule mining under dynamic threshold. Applied Sciences, 9(24), 5398. https://doi.org/10.3390/app9245398
   Google Scholar
• Ateca-Amestoy, V. (2008). Determining heterogeneous behavior for theater attendance. Journal of Cultural Economics, 32(2), 127–151. https://doi.org/10.1007/s10824-008-9065-z
   Web of Science ®Google Scholar
• Ateca-Amestoy, V. M., Ginsburgh, V., Mazza, I., O’Hagan, J., & Prieto-Rodriguez, J. (2017). Enhancing participation in the arts in the EU. Springer.
   Google Scholar
• Barbieri, C., & Mahoney, E. (2010). Cultural tourism behaviour and preferences among the live‐performing arts audience: An application of the univorous–omnivorous framework. International Journal of Tourism Research, 12(5), 481–496. https://doi.org/10.1002/jtr.767
   Web of Science ®Google Scholar
• Berry, M. J., & Linoff, G. S. (2004). Data mining Techniques: For marketing, sales, and customer relationship management. John Wiley & Sons.
   Google Scholar
• Bonazzi, M., Cancellieri, G., & Casarin, F. (2024). Omnivorous cultural consumption and the co-creation of cultural products: Interactive versus participatory art. Journal of Consumer Culture, 24(1), 24–44. https://doi.org/10.1177/14695405231181510
   Web of Science ®Google Scholar
• Borgonovi, F. (2004). Performing arts attendance: An economic approach. Applied Economics, 36(17), 1871–1885. https://doi.org/10.1080/0003684042000264010
   Web of Science ®Google Scholar
• Breiman, L. (2001). Statistical modeling: The two cultures (with comments and a rejoinder by the author). Statistical Science, 16(3), 199–231. https://doi.org/10.1214/ss/1009213726
   Web of Science ®Google Scholar
• Cappellari, L., & Jenkins, S. P. (2003). Multivariate probit regression using simulated maximum likelihood. The STATA Journal, 3(3), 278–294. https://doi.org/10.1177/1536867X0300300305
   Web of Science ®Google Scholar
• Carmichael, I., & Marron, J. S. (2018). Data science vs. statistics: two cultures? Japanese Journal of Statistics and Data Science, 1(1), 117–138. https://doi.org/10.1007/s42081-018-0009-3
   Google Scholar
• Chang, H.-H., & Cheng, T.-C. (2022). Omnivorous audiences of performing arts: Evidence from Taiwan. Creative Industries Journal, 15(2), 160–175. https://doi.org/10.1080/17510694.2021.1938924
   Google Scholar
• Cheng, T.-C., & Wen, W.-J. (2011). Determinants of performing arts attendance in Taiwan: A multivariate probit analysis. Applied Economics Letters, 18(15), 1437–1442. https://doi.org/10.1080/13504851.2010.543060
   Web of Science ®Google Scholar
• Cil, I. (2012). Consumption universes based supermarket layout through association rule mining and multidimensional scaling. Expert Systems with Applications, 39(10), 8611–8625. https://doi.org/10.1016/j.eswa.2012.01.192
   Web of Science ®Google Scholar
• Czibula, G., Czibula, I. G., Miholca, D. L., & Crivei, L. M. (2019). A novel concurrent relational association rule mining approach. Expert Systems with Applications, 125, 142–156. https://doi.org/10.1016/j.eswa.2019.01.082
   Web of Science ®Google Scholar
• DiMaggio, P., & Mukhtar, T. (2012). Arts participation as cultural capital in the United States, 1982–2002: Signs of decline? In Engaging art (pp. 273–305). Routledge.
   Google Scholar
• Griva, A., Bardaki, C., Pramatari, K., & Papakiriakopoulos, D. (2018). Retail business analytics: Customer visit segmentation using market basket data. Expert Systems with Applications, 100, 1–16. https://doi.org/10.1016/j.eswa.2018.01.029
   Web of Science ®Google Scholar
• Hahsler, M., Grün, B., & Hornik, K. (2005). Arules – a computational environment for mining association rules and frequent item sets. Journal of Statistical Software, 14(15), 1–25. https://doi.org/10.18637/jss.v014.i15
   Web of Science ®Google Scholar
• Hahsler, M., & Karpienko, R. (2017). Visualizing association rules in hierarchical groups. Journal of Business Economics, 87(3), 317–335. https://doi.org/10.1007/s11573-016-0822-8
   Google Scholar
• Hallmann, K., Artime, C. M., Breuer, C., Dallmeyer, S., & Metz, M. (2017). Leisure participation: Modelling the decision to engage in sports and culture. Journal of Cultural Economics, 41(4), 467–487. https://doi.org/10.1007/s10824-016-9275-8
   Web of Science ®Google Scholar
• Hazir, I. K., & Warde, A. (2015). The cultural omnivore thesis: Methodological aspects of the debate. In Routledge international handbook of the sociology of art and culture (pp. 77–89). Routledge.
   Google Scholar
• Heilbrun, J., & Gray, C. M. (2001). The economics of art and culture. Cambridge University Press.
   Google Scholar
• Jain, N., & Srivastava, V. (2013). Data mining techniques: a survey paper. IJRET: International Journal of Research in Engineering and Technology, 2(11), 2319–1163.
   Google Scholar
• Khare, S., & Gupta, D. (2016). Association rule analysis in cardiovascular disease. In 2016 Second International Conference on Cognitive Computing and Information Processing (CCIP), Mysuru, India (pp. 1–6). IEEE.
   Google Scholar
• Lim, C. C., & Bendle, L. J. (2012). Arts tourism in Seoul: Tourist-orientated performing arts as a sustainable niche market. Journal of Sustainable Tourism, 20(5), 667–682. https://doi.org/10.1080/09669582.2011.636817
   Web of Science ®Google Scholar
• Liu, X., Niu, X., & Fournier-Viger, P. (2021). Fast top-k association rule mining using rule generation property pruning. Applied Intelligence, 51(4), 2077–2093. https://doi.org/10.1007/s10489-020-01994-9
   Web of Science ®Google Scholar
• Manchanda, P., Ansari, A., & Gupta, S. (1999). The “shopping basket”: A model for multicategory purchase incidence decisions. Marketing Science, 18(2), 95–114. https://doi.org/10.1287/mksc.18.2.95
   Web of Science ®Google Scholar
• Mild, A., & Reutterer, T. (2003). An improved collaborative filtering approach for predicting cross-category purchases based on binary market basket data. Journal of Retailing and Consumer Services, 10(3), 123–133. https://doi.org/10.1016/S0969-6989(03)00003-1
   Google Scholar
• Moodley, R., Chiclana, F., Caraffini, F., & Carter, J. (2019). Application of uninorms to market basket analysis. International Journal of Intelligent Systems, 34(1), 39–49. https://doi.org/10.1002/int.22039
   Web of Science ®Google Scholar
• Nahar, J., Imam, T., Tickle, K. S., & Chen, Y. P. P. (2013). Association rule mining to detect factors which contribute to heart disease in males and females. Expert Systems with Applications, 40(4), 1086–1093. https://doi.org/10.1016/j.eswa.2012.08.028
   Web of Science ®Google Scholar
• Osman, M. T., Yuli, C., Li, T., & Senin, S. F. (2021). Association rule mining for identification of port state control patterns in Malaysian ports. Maritime Policy & Management, 48(8), 1082–1095. https://doi.org/10.1080/03088839.2020.1825854
   Web of Science ®Google Scholar
• Peterson, R. A. (1992). Understanding audience segmentation: From elite and mass to omnivore and univore. Poetics, 21(4), 243–258. https://doi.org/10.1016/0304-422X(92)90008-Q
   Web of Science ®Google Scholar
• Powers, D. A., & Xie, Y. (2008). Statistical methods for categorical data analysis (2nd ed.). Emerald.
   Google Scholar
• Seaman, B. A. (2005). Attendance and public participation in the performing arts: A review of the empirical literature. Andrew Young School of Policy Studies Research Paper Series, (06–25).
   Google Scholar
• Shaw, M. J., Subramaniam, C., Tan, G. W., & Welge, M. E. (2001). Knowledge management and data mining for marketing. Decision Support Systems, 31(1), 127–137. https://doi.org/10.1016/S0167-9236(00)00123-8
   Web of Science ®Google Scholar
• Sintas, J. L., & Álvarez, E. G. (2004). Omnivore versus univore consumption and its symbolic properties: Evidence from Spaniards’ performing arts attendance. Poetics, 32(6), 471–491. https://doi.org/10.1016/S0304-422X(04)00055-5
   Web of Science ®Google Scholar
• Sullivan, O., & Katz-Gerro, T. (2007). The omnivore thesis revisited: Voracious cultural consumers. European Sociological Review, 23(2), 123–137. https://doi.org/10.1093/esr/jcl024
   Web of Science ®Google Scholar
• Tandan, M., Acharya, Y., Pokharel, S., & Timilsina, M. (2021). Discovering symptom patterns of COVID-19 patients using association rule mining. Computers in Biology and Medicine, 131, 104249. https://doi.org/10.1016/j.compbiomed.2021.104249
   PubMed Web of Science ®Google Scholar
• Wen, W.-J., & Cheng, T.-C. (2013). Performing arts attendance in Taiwan: Who and how often? Journal of Cultural Economics, 37(2), 309–325. https://doi.org/10.1007/s10824-012-9180-8
   Web of Science ®Google Scholar
• Withers, G. A. (1980). Unbalanced growth and the demand for performing arts: An econometric analysis. Southern Economic Journal, 46(3), 735–742. https://doi.org/10.2307/1057143
   Web of Science ®Google Scholar
• Yu, K., Choi, H., & Ning, X. (2022). On the positioning and market selection of opera performance art based on industrial data mining. Wireless Communications and Mobile Computing, 2022, 1–10. https://doi.org/10.1155/2022/4141355
   Web of Science ®Google Scholar
• Zanola, R. (2010). Major influences on circus attendance. Empirical Economics, 38(1), 159–170. https://doi.org/10.1007/s00181-009-0260-3
   Web of Science ®Google Scholar
• Zieba, M. (2009). Full-income and price elasticities of demand for German public theatre. Journal of Cultural Economics, 33(2), 85–108. https://doi.org/10.1007/s10824-009-9094-2
   Web of Science ®Google Scholar