Department: MSc Cybersecurity Science and Applications
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.
Sarstedt, M. & Mooi, E. (2014). 'Cluster analysis', in A concise guide to market research. Springer, Berlin, Heidelberg, pp. 273-324.
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. & 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.