publication . Preprint . 2017

A Comparative Quantitative Analysis of Contemporary Big Data Clustering Algorithms for Market Segmentation in Hospitality Industry

Bose, Avishek; Munir, Arslan; Shabani, Neda;
Open Access English
  • Published: 18 Sep 2017
Abstract
The hospitality industry is one of the data-rich industries that receives huge Volumes of data streaming at high Velocity with considerably Variety, Veracity, and Variability. These properties make the data analysis in the hospitality industry a big data problem. Meeting the customers' expectations is a key factor in the hospitality industry to grasp the customers' loyalty. To achieve this goal, marketing professionals in this industry actively look for ways to utilize their data in the best possible manner and advance their data analytic solutions, such as identifying a unique market segmentation clustering and developing a recommendation system. In this paper,...
Subjects
free text keywords: Computer Science - Databases, Computer Science - Artificial Intelligence
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21 references, page 1 of 2

[1] Wind, Jerry, and David Bell. "Market segmentation." The marketing book (2007): 222-244.

[2] Huang, Zan, Daniel Zeng, and Hsinchun Chen. "A comparison of collaborative-filtering recommendation algorithms for e-commerce." IEEE Intelligent Systems 22.5 (2007).

[3] Linden, Greg, Brent Smith, and Jeremy York. "Amazon.com recommendations: Item-to-item collaborative filtering." IEEE Internet computing 7.1 (2003): 76-80.

[4] Mowforth, M., & Munt, I. (2015). Tourism and sustainability: Development, globalisation and new tourism in the third world. Routledge. [OpenAIRE]

[5] Nagaraju, S., Kashyap, M., & Bhattachraya, M. (2017). An effective density based approach to detect complex data clusters using notion of neighborhood difference. International Journal of Automation and Computing, 14(1), 57-67.

[6] Cho, Y. H., Kim, J. K., & Kim, S. H. (2002). A personalized recommender system based on web usage mining and decision tree induction. Expert systems with Applications, 23(3), 329-342.

[7] Göksedef, M., & Gündüz-Öğüdücü, Ş. (2010). Combination of Web page recommender systems. Expert Systems with Applications, 37(4), 2911- 2922. [OpenAIRE]

[8] Chou, P. H., Li, P. H., Chen, K. K., & Wu, M. J. (2010). Integrating web mining and neural network for personalized e-commerce automatic service. Expert Systems with Applications, 37(4), 2898-2910.

[9] Punj, G., & Stewart, D. W. (1983). Cluster analysis in marketing research: Review and suggestions for application. Journal of marketing research, 134-148.

[10] Prayag, G., Disegna, M., Cohen, S. A., & Yan, H. (2015). Segmenting markets by bagged clustering: Young Chinese travelers to Western Europe. Journal of Travel Research, 54(2), 234-250.

[11] Xiang, Z., Schwartz, Z., Gerdes, J. H., & Uysal, M. (2015). What can big data and text analytics tell us about hotel guest experience and satisfaction?. International Journal of Hospitality Management, 44, 120- 130.

[12] Zhou, L., Ye, S., Pearce, P. L., & Wu, M. Y. (2014). Refreshing hotel satisfaction studies by reconfiguring customer review data. International Journal of Hospitality Management, 38, 1-10.

[13] Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2000, October). Analysis of recommendation algorithms for e-commerce. In Proceedings of the 2nd ACM conference on Electronic commerce (pp. 158-167). ACM.

[14] Ester, M., Kriegel, H. P., Sander, J., & Xu, X. (1996, August). A densitybased algorithm for discovering clusters in large spatial databases with noise. In Kdd (Vol. 96, No. 34, pp. 226-231).

[15] Ankerst, M., Breunig, M. M., Kriegel, H. P., & Sander, J. (1999, June). OPTICS: ordering points to identify the clustering structure. In ACM Sigmod record (Vol. 28, No. 2, pp. 49-60). ACM. [OpenAIRE]

21 references, page 1 of 2
Abstract
The hospitality industry is one of the data-rich industries that receives huge Volumes of data streaming at high Velocity with considerably Variety, Veracity, and Variability. These properties make the data analysis in the hospitality industry a big data problem. Meeting the customers' expectations is a key factor in the hospitality industry to grasp the customers' loyalty. To achieve this goal, marketing professionals in this industry actively look for ways to utilize their data in the best possible manner and advance their data analytic solutions, such as identifying a unique market segmentation clustering and developing a recommendation system. In this paper,...
Subjects
free text keywords: Computer Science - Databases, Computer Science - Artificial Intelligence
Download from
21 references, page 1 of 2

[1] Wind, Jerry, and David Bell. "Market segmentation." The marketing book (2007): 222-244.

[2] Huang, Zan, Daniel Zeng, and Hsinchun Chen. "A comparison of collaborative-filtering recommendation algorithms for e-commerce." IEEE Intelligent Systems 22.5 (2007).

[3] Linden, Greg, Brent Smith, and Jeremy York. "Amazon.com recommendations: Item-to-item collaborative filtering." IEEE Internet computing 7.1 (2003): 76-80.

[4] Mowforth, M., & Munt, I. (2015). Tourism and sustainability: Development, globalisation and new tourism in the third world. Routledge. [OpenAIRE]

[5] Nagaraju, S., Kashyap, M., & Bhattachraya, M. (2017). An effective density based approach to detect complex data clusters using notion of neighborhood difference. International Journal of Automation and Computing, 14(1), 57-67.

[6] Cho, Y. H., Kim, J. K., & Kim, S. H. (2002). A personalized recommender system based on web usage mining and decision tree induction. Expert systems with Applications, 23(3), 329-342.

[7] Göksedef, M., & Gündüz-Öğüdücü, Ş. (2010). Combination of Web page recommender systems. Expert Systems with Applications, 37(4), 2911- 2922. [OpenAIRE]

[8] Chou, P. H., Li, P. H., Chen, K. K., & Wu, M. J. (2010). Integrating web mining and neural network for personalized e-commerce automatic service. Expert Systems with Applications, 37(4), 2898-2910.

[9] Punj, G., & Stewart, D. W. (1983). Cluster analysis in marketing research: Review and suggestions for application. Journal of marketing research, 134-148.

[10] Prayag, G., Disegna, M., Cohen, S. A., & Yan, H. (2015). Segmenting markets by bagged clustering: Young Chinese travelers to Western Europe. Journal of Travel Research, 54(2), 234-250.

[11] Xiang, Z., Schwartz, Z., Gerdes, J. H., & Uysal, M. (2015). What can big data and text analytics tell us about hotel guest experience and satisfaction?. International Journal of Hospitality Management, 44, 120- 130.

[12] Zhou, L., Ye, S., Pearce, P. L., & Wu, M. Y. (2014). Refreshing hotel satisfaction studies by reconfiguring customer review data. International Journal of Hospitality Management, 38, 1-10.

[13] Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2000, October). Analysis of recommendation algorithms for e-commerce. In Proceedings of the 2nd ACM conference on Electronic commerce (pp. 158-167). ACM.

[14] Ester, M., Kriegel, H. P., Sander, J., & Xu, X. (1996, August). A densitybased algorithm for discovering clusters in large spatial databases with noise. In Kdd (Vol. 96, No. 34, pp. 226-231).

[15] Ankerst, M., Breunig, M. M., Kriegel, H. P., & Sander, J. (1999, June). OPTICS: ordering points to identify the clustering structure. In ACM Sigmod record (Vol. 28, No. 2, pp. 49-60). ACM. [OpenAIRE]

21 references, page 1 of 2
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