BUiD Library: AI 502 Data Mining and Exploration: Reading list

AI 502 Data Mining and Exploration

Department: MSc Artificial Intelligence

Module Description: Data mining is about analyzing, interpreting, visualizing and exploiting the data that is captured scientific and commercial environments. This module provides students with an opportunity to gain an in depth understanding of the theories and issues related to mining and exploring data, ranging from statistical summaries, to visualization, to classification and clustering. Practical case studies will be used for illustration.

Module texts

Tan, P., Steinbach, M. & Kumar, V. (2019). Introduction to data mining. 2nd edn. Boston, MA: Addison Wesley.

Recommended readings

Abdallah, S. (2018). An intelligent system for identifying influential words in real-estate classifieds. Journal of Intelligent Systems, vol. 27(2), pp. 183-194.
L'Heureux, A., Grolinger, K., ElYamany, H. F. & Capretz, M. (2017). Machine learning with big data: challenges and approaches. IEEE Access.
Sapountzi, A. & Psannis, K.E. (2016). Social networking data analysis tools & challenges. Future Generation Computer Systems.
Sarstedt, M. & Mooi, E. (2014). 'Cluster analysis', in A concise guide to market research. Springer, Berlin, Heidelberg, pp. 273-324.
Yong-Yeol Ahn, Sebastian E. Ahnert, James P. Bagrow & Albert-László Barabási (2011). Flavor network and the principles of food pairing. Scientific Reports 1, Article number: 196 doi:10.1038/srep00196
Acerbi A, Lampos V, Garnett P. & Bentley RA (2013). The expression of emotions in 20th century books. PLoS ONE, vol. 8(3): e59030. doi:10.1371/journal.pone.0059030
Boccaletti, S., Latora, V., Moreno, Y., Chavez, M. & Hwang, D. U. (2006). Complex networks: structure and dynamics. Physics Reports, vol. 424(4-5), Section 2.1.1.
Fawcett, T. (2004). ROC graphs: notes and practical considerations for researchers [online]. Section 1 and 2. Available at: http://binf.gmu.edu/mmasso/ROC101.pdf
Boccaletti, S., Latora, V., Moreno, Y., Chavez, M. & Hwang, D.U. (2006). Complex networks: structure and dynamics. Physics Reports. Vol. 424 (4-5), February 2006, Section 2, 4 and 6.
Keogh, E. (2002a). ‘Exact indexing of dynamic time warping’, in 28th International Conference on Very Large Data Bases. Hong Kong, pp 406-417.
Keogh, E. & Kasetty, S. (2002b). ‘On the need for time series data mining benchmarks: a survey and empirical demonstration’, in The 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. July 23 - 26, 2002. Edmonton, Alberta, Canada, pp 102-111.
Lin, J., Keogh, E., Lonardi, S., Lankford, J. P. & Nystrom, D. M. (2004). ‘Visually mining and monitoring massive time series’, in Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Aug 22-25. Seattle, WA.
Ratanamahatana, C.A. & Keogh, E. (2004). ‘Everything you know about dynamic time warping is wrong’ in the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2004): Third Workshop on Mining Temporal and Sequential Data, August 22-25, Seattle, WA.