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題名 台灣民眾各類文化活動參與之關聯分析
Association Analysis for Participation in Various Cultural Activities in Taiwan.
作者 鄭雲昊
Zheng, Yun-Hao
貢獻者 鄭宗記
鄭雲昊
Zheng, Yun-Hao
關鍵詞 文化雜食
資料採掘
市場區隔
關聯規則分析
對應分析
多重對應分析
多維標度
日期 2024
上傳時間 5-八月-2024 14:00:04 (UTC+8)
摘要 過去的人們對於文化的接納及品味，認為高經濟地位或相對應關係之民眾其對於文化喜好具單一性，但在Peterson & Kern (1996) 研究中發現，大眾的文化習性隨著時間推進漸漸地越來越多元化，而現在有許多學者針對於不同文化具一定程度的接受度或積極性之情況為文化雜食傾向 (Cultural Omnivore)。 本文將透過資料採掘中之關聯規則分析(Association Rules Mining)、常用於區隔不同消費者群體間相關性的統計方法對應分析(Correspondence Analysis)和多重對應分析(Multiple Correspondence Analysis)以及探討兩變數間距離的相關性分析方法多維標度(Multidimensional Scaling)等方法，來研究出台灣民眾的文化參與情形，並利用台灣民眾的社會經濟地位作為市場區隔標準，分析不同群體所偏好參與的文化活動。
參考文獻 Abdi, H., & Valentin, D. (2007). Multiple correspondence analysis. Encyclopedia of Measurement and Statistics, 2(4), 651–657. Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. In Proceedings of The 1993 ACM SIGMOD International Conference on Management of Data (pp. 207–216). Agrawal, R., Srikant, R., et al. (1994). Fast algorithms for mining association rules. In Proc. 20th Int. Conf. Very Large Data Bases, VLDB (Vol. 1215, pp. 487–499). 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. Brin, S., Motwani, R., Ullman, J. D., & Tsur, S. (1997). Dynamic itemset counting and implication rules for market basket data. In Proceedings of The 1997 ACM SIGMOD International Conference on Management of Data (pp. 255–264). Cheng, T.-C., & Huang, C.-H. (2024). Mining and modeling both association patterns and determinants for the attendance of performing arts activities. Measurement: Inter- disciplinary Research and Perspectives, 1–19. Graffelman, J., & De Leeuw, J. (2023). Improved approximation and visualization of the correlation matrix. The American Statistician, 77(4), 432–442. Greenacre, M. (2017). Correspondence analysis in practice. Chapman and Hall/CRC. Gunnarsson, C. L., Walker, M. M., Walatka, V., & Swann, K. (2007). Lessons learned: A case study using data mining in the newspaper industry. Journal of Database Marketing & Customer Strategy Management, 14, 271–280. Han, J., Pei, J., & Tong, H. (2022). Data mining: concepts and techniques. Morgan Kaufmann. Hedegard, D. (2013). Blackness and experience in omnivorous cultural consumption: Evidence from the tourism of capoeira in salvador, brazil. Poetics, 41(1), 1–26. Higgs, N. (1991). Practical and innovative uses of correspondence analysis. Journal of The Royal Statistical Society: Series D (The Statistician), 40(2), 183–194. Hills, M. (1969). On looking at large correlation matrices. Biometrika, 56(2), 249–253. Holt, D. B. (1998). Does cultural capital structure american consumption? Journal of Consumer Research, 25(1), 1–25. Kishino, H., Hanyu, K., Yamashita, H., & Hayashi, C. (1998). Correspondence analysis of paper recycling society: consumers and paper makers in japan. Resources, Conser- vation and Recycling, 23(4), 193–208. Kwon, Y. J., & Kwon, K.-N. (2013). Cultural omnivores’ consumption: strategic and inclusively exclusive. International Journal of Marketing Studies, 5(1), 118. Lin, R., Lin, C., & Wong, J. (1996). An application of multidimensional scaling in product semantics. International Journal of Industrial Ergonomics, 18(2-3), 193–204. Major, J. A., & Mangano, J. J. (1995). Selecting among rules induced from a hurricane database. Journal of Intelligent Information Systems, 4, 39–52. McCrae, R. R., & Greenberg, D. M. (2014). Openness to experience. The Wiley Handbook of Genius, 222–243. Moser, E. B. (1989). Exploring contingency tables with correspondence analysis. Bioin- formatics, 5(3), 183–189. Ollivier, M. (2008). Modes of openness to cultural diversity: Humanist, populist, practi- cal, and indifferent. Poetics, 36(2-3), 120–147. Peterson, R. A. (1992). Understanding audience segmentation: From elite and mass to omnivore and univore. Poetics, 21(4), 243–258. Peterson, R. A., & Kern, R. M. (1996). Changing highbrow taste: From snob to omnivore. American Sociological Review, 900–907. Rimmer, M. (2012). Beyond omnivores and univores: The promise of a concept of musical habitus. Cultural Sociology, 6(3), 299–318. Romão, J., Neuts, B., Nijkamp, P., & Van Leeuwen, E. (2015). Culture, product differenti- ation and market segmentation: A structural analysis of the motivation and satisfaction of tourists in amsterdam. Tourism Economics, 21(3), 455–474. Roose, H., Van Eijck, K., & Lievens, J. (2012). Culture of distinction or culture of openness? using a social space approach to analyze the social structuring of lifestyles. Poetics, 40(6), 491–513. Saeed, N., Nam, H., Al-Naffouri, T. Y., & Alouini, M.-S. (2019). A state-of-the-art sur- vey on multidimensional scaling-based localization techniques. IEEE Communications Surveys & Tutorials, 21(4), 3565–3583. Saeed, N., Nam, H., Haq, M. I. U., & Muhammad Saqib, D. B. (2018). A survey on multidimensional scaling. ACM Computing Surveys (CSUR), 51(3), 1–25. Sarkar, D., Bali, R., Sharma, T., Sarkar, D., Bali, R., & Sharma, T. (2018). Customer segmentation and effective cross selling. Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems, 373–405. Saunders, J. A. (1980). Cluster analysis for market segmentation. European Journal of Marketing, 14(7), 422–435. Streifer, P. A., & Schumann, J. A. (2005). Using data mining to identify actionable infor- mation: Breaking new ground in data-driven decision making. Journal of Education for Students Placed at Risk, 10(3), 281–293. Sullivan, O., & Katz-Gerro, T. (2007). The omnivore thesis revisited: Voracious cultural consumers. European Sociological Review, 23(2), 123–137. Tynan, A. C., & Drayton, J. (1987). Market segmentation. Journal of Marketing Man- agement, 2(3), 301–335. Ünvan, Y. A. (2021). Market basket analysis with association rules. Communications in Statistics-Theory and Methods, 50(7), 1615–1628. Warde, A., Wright, D., & Gayo-Cal, M. (2007). Understanding cultural omnivorousness: Or, the myth of the cultural omnivore. Cultural Sociology, 1(2), 143–164. Warde, A., Wright, D., & Gayo-Cal, M. (2008). The omnivorous orientation in the uk. Poetics, 36(2-3), 148–165. Zhou, N. (2020). Database design of regional music characteristic culture resources based on improved neural network in data mining. Personal and Ubiquitous Computing, 24(1), 103–114.
描述 碩士
國立政治大學
統計學系
111354022
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0111354022
資料類型 thesis
dc.contributor.advisor 鄭宗記zh_TW
dc.contributor.author (作者) 鄭雲昊zh_TW
dc.contributor.author (作者) Zheng, Yun-Haoen_US
dc.creator (作者) 鄭雲昊zh_TW
dc.creator (作者) Zheng, Yun-Haoen_US
dc.date (日期) 2024en_US
dc.date.accessioned 5-八月-2024 14:00:04 (UTC+8)-
dc.date.available 5-八月-2024 14:00:04 (UTC+8)-
dc.date.issued (上傳時間) 5-八月-2024 14:00:04 (UTC+8)-
dc.identifier (其他 識別碼) G0111354022en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/152779-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 統計學系zh_TW
dc.description (描述) 111354022zh_TW
dc.description.abstract (摘要) 過去的人們對於文化的接納及品味，認為高經濟地位或相對應關係之民眾其對於文化喜好具單一性，但在Peterson & Kern (1996) 研究中發現，大眾的文化習性隨著時間推進漸漸地越來越多元化，而現在有許多學者針對於不同文化具一定程度的接受度或積極性之情況為文化雜食傾向 (Cultural Omnivore)。 本文將透過資料採掘中之關聯規則分析(Association Rules Mining)、常用於區隔不同消費者群體間相關性的統計方法對應分析(Correspondence Analysis)和多重對應分析(Multiple Correspondence Analysis)以及探討兩變數間距離的相關性分析方法多維標度(Multidimensional Scaling)等方法，來研究出台灣民眾的文化參與情形，並利用台灣民眾的社會經濟地位作為市場區隔標準，分析不同群體所偏好參與的文化活動。zh_TW
dc.description.tableofcontents 第一章、緒論 1 第 一 節、研究背景與動機 1 第 二 節、研究目的 3 第 三 節、研究架構 4 第二章、文獻探討 5 第 一 節、資料採掘 5 第 二 節、市場區隔和交叉銷售 6 第 三 節、關聯規則分析 6 第 四 節、對應分析 8 第 五 節、多重對應分析 10 第 六 節、多維標度 12 第三章、實證研究 14 第 一 節、資料來源 14 第 二 節、資料收集 15 第 三 節、現場文化參與分析 22 第 四 節、線上文化參與分析 40 第 五 節、對應分析 70 第 六 節、多重對應分析 103 第 七 節、多維標度分析 108 第四章、結論與建議 139 參考文獻 141zh_TW
dc.format.extent 5948505 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0111354022en_US
dc.subject (關鍵詞) 文化雜食zh_TW
dc.subject (關鍵詞) 資料採掘zh_TW
dc.subject (關鍵詞) 市場區隔zh_TW
dc.subject (關鍵詞) 關聯規則分析zh_TW
dc.subject (關鍵詞) 對應分析zh_TW
dc.subject (關鍵詞) 多重對應分析zh_TW
dc.subject (關鍵詞) 多維標度zh_TW
dc.title (題名) 台灣民眾各類文化活動參與之關聯分析zh_TW
dc.title (題名) Association Analysis for Participation in Various Cultural Activities in Taiwan.en_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Abdi, H., & Valentin, D. (2007). Multiple correspondence analysis. Encyclopedia of Measurement and Statistics, 2(4), 651–657. Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. In Proceedings of The 1993 ACM SIGMOD International Conference on Management of Data (pp. 207–216). Agrawal, R., Srikant, R., et al. (1994). Fast algorithms for mining association rules. In Proc. 20th Int. Conf. Very Large Data Bases, VLDB (Vol. 1215, pp. 487–499). 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. Brin, S., Motwani, R., Ullman, J. D., & Tsur, S. (1997). Dynamic itemset counting and implication rules for market basket data. In Proceedings of The 1997 ACM SIGMOD International Conference on Management of Data (pp. 255–264). Cheng, T.-C., & Huang, C.-H. (2024). Mining and modeling both association patterns and determinants for the attendance of performing arts activities. Measurement: Inter- disciplinary Research and Perspectives, 1–19. Graffelman, J., & De Leeuw, J. (2023). Improved approximation and visualization of the correlation matrix. The American Statistician, 77(4), 432–442. Greenacre, M. (2017). Correspondence analysis in practice. Chapman and Hall/CRC. Gunnarsson, C. L., Walker, M. M., Walatka, V., & Swann, K. (2007). Lessons learned: A case study using data mining in the newspaper industry. Journal of Database Marketing & Customer Strategy Management, 14, 271–280. Han, J., Pei, J., & Tong, H. (2022). Data mining: concepts and techniques. Morgan Kaufmann. Hedegard, D. (2013). Blackness and experience in omnivorous cultural consumption: Evidence from the tourism of capoeira in salvador, brazil. Poetics, 41(1), 1–26. Higgs, N. (1991). Practical and innovative uses of correspondence analysis. Journal of The Royal Statistical Society: Series D (The Statistician), 40(2), 183–194. Hills, M. (1969). On looking at large correlation matrices. Biometrika, 56(2), 249–253. Holt, D. B. (1998). Does cultural capital structure american consumption? Journal of Consumer Research, 25(1), 1–25. Kishino, H., Hanyu, K., Yamashita, H., & Hayashi, C. (1998). Correspondence analysis of paper recycling society: consumers and paper makers in japan. Resources, Conser- vation and Recycling, 23(4), 193–208. Kwon, Y. J., & Kwon, K.-N. (2013). Cultural omnivores’ consumption: strategic and inclusively exclusive. International Journal of Marketing Studies, 5(1), 118. Lin, R., Lin, C., & Wong, J. (1996). An application of multidimensional scaling in product semantics. International Journal of Industrial Ergonomics, 18(2-3), 193–204. Major, J. A., & Mangano, J. J. (1995). Selecting among rules induced from a hurricane database. Journal of Intelligent Information Systems, 4, 39–52. McCrae, R. R., & Greenberg, D. M. (2014). Openness to experience. The Wiley Handbook of Genius, 222–243. Moser, E. B. (1989). Exploring contingency tables with correspondence analysis. Bioin- formatics, 5(3), 183–189. Ollivier, M. (2008). Modes of openness to cultural diversity: Humanist, populist, practi- cal, and indifferent. Poetics, 36(2-3), 120–147. Peterson, R. A. (1992). Understanding audience segmentation: From elite and mass to omnivore and univore. Poetics, 21(4), 243–258. Peterson, R. A., & Kern, R. M. (1996). Changing highbrow taste: From snob to omnivore. American Sociological Review, 900–907. Rimmer, M. (2012). Beyond omnivores and univores: The promise of a concept of musical habitus. Cultural Sociology, 6(3), 299–318. Romão, J., Neuts, B., Nijkamp, P., & Van Leeuwen, E. (2015). Culture, product differenti- ation and market segmentation: A structural analysis of the motivation and satisfaction of tourists in amsterdam. Tourism Economics, 21(3), 455–474. Roose, H., Van Eijck, K., & Lievens, J. (2012). Culture of distinction or culture of openness? using a social space approach to analyze the social structuring of lifestyles. Poetics, 40(6), 491–513. Saeed, N., Nam, H., Al-Naffouri, T. Y., & Alouini, M.-S. (2019). A state-of-the-art sur- vey on multidimensional scaling-based localization techniques. IEEE Communications Surveys & Tutorials, 21(4), 3565–3583. Saeed, N., Nam, H., Haq, M. I. U., & Muhammad Saqib, D. B. (2018). A survey on multidimensional scaling. ACM Computing Surveys (CSUR), 51(3), 1–25. Sarkar, D., Bali, R., Sharma, T., Sarkar, D., Bali, R., & Sharma, T. (2018). Customer segmentation and effective cross selling. Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems, 373–405. Saunders, J. A. (1980). Cluster analysis for market segmentation. European Journal of Marketing, 14(7), 422–435. Streifer, P. A., & Schumann, J. A. (2005). Using data mining to identify actionable infor- mation: Breaking new ground in data-driven decision making. Journal of Education for Students Placed at Risk, 10(3), 281–293. Sullivan, O., & Katz-Gerro, T. (2007). The omnivore thesis revisited: Voracious cultural consumers. European Sociological Review, 23(2), 123–137. Tynan, A. C., & Drayton, J. (1987). Market segmentation. Journal of Marketing Man- agement, 2(3), 301–335. Ünvan, Y. A. (2021). Market basket analysis with association rules. Communications in Statistics-Theory and Methods, 50(7), 1615–1628. Warde, A., Wright, D., & Gayo-Cal, M. (2007). Understanding cultural omnivorousness: Or, the myth of the cultural omnivore. Cultural Sociology, 1(2), 143–164. Warde, A., Wright, D., & Gayo-Cal, M. (2008). The omnivorous orientation in the uk. Poetics, 36(2-3), 148–165. Zhou, N. (2020). Database design of regional music characteristic culture resources based on improved neural network in data mining. Personal and Ubiquitous Computing, 24(1), 103–114.zh_TW