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Data Science

Data Science studies the tools and methods to organise and process data sets. As data sets become larger and more interconnected, they carry a greater potential for detecting patterns that prove useful for science, industry, and healthcare. Yet large sets of complex data, often composed of both structured and unstructured data, also present significant challenges with regard to organising, storing, and processing them.

The Data Sciences Research Group studies the use of large databases, distributed databases, and cloud computing. We also develop and adopt various methods including evolutionary computing, statistical methods, visualisation, agents, pattern recognition, and machine learning.
Our work and that of our postgraduate students directly informs our teaching in the Master of Business Data Science (MBusDataSci).

Group Members

Core members:

Adjunct members:

Selected Publications

  • Dick, G. (2012). Niche allocation in spatially-structured evolutionary algorithms with gradients. Proceedings of the IEEE Congress on Evolutionary Computation (CEC). doi: 10.1109/CEC.2012.6256542
  • Dick, G., & Whigham, P. A. (2011). Weighted local sharing and local clearing for multimodal optimisation. Soft Computing, 15, 1707-1721. doi: 10.1007/s00500-010-0612-0
  • Whigham, P.A. & Withanawasam, R. "Evolving a Robust Trader in a Cyclic Double Auction Market", GECCO’11 Proceedings of the 13th annual conference on Genetic and Evolutionary computation. 1451 – 1458, (2011).
  • Whigham, P.A., Withanawasam R., Crack, T. and I.M. Premachandra. "Evolving trading strategies for a limit-order book generator”, WCCI 2010 IEEE World Congress on Computational Intelligence July, 18-23, 2010 - CCIB, Barcelona, Spain, 2467 - 2474 (2010). http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5586114
  • Whigham, P. A., & Dick, G. (2010). Implicitly controlling bloat in genetic programming. IEEE Transactions on Evolutionary Computation, 14(2), 173-190. http://dx.doi.org/10.1109/tevc.2009.2027314
  • Dick, G. (2010). Automatic identification of the niche radius using spatially-structured clearing methods. Proceedings of the IEEE Congress on Evolutionary Computation (CEC), (pp. 1264-1271). IEEE. http://dx.doi.org/10.1109/CEC.2010.5586085
  • Dick, G. (2010). The utility of scale factor adaptation in differential evolution. Proceedings of the IEEE Congress on Evolutionary Computation (CEC), (pp. 4355-4362). IEEE. http://dx.doi.org/10.1109/CEC.2010.5586480
  • Woodford, B. J. (2010). Automatic optimization of pruning in evolving fuzzy neural networks using an entropy measure. Proceedings of the IEEE World Congress on Computational Intelligence (WCCI), (pp. 1053-1059). IEEE. http://dx.doi.org/10.1109/IJCNN.2010.5596728
  • Whigham, P. A., Aldridge, C., & de Lange, M. (2009). Constrained evolutionary art: Interactive flag design. Proceedings of the IEEE Congress on Evolutionary Computation, (pp. 2194-2200). IEEE. http://dx.doi.org/10.1109/CEC.2009.4983213
  • McKay, R. I., Hoai, N. X., Whigham, P. A., Shan, Y., & O'Neill, M. (2010). Grammar-based genetic programming: A survey. Genetic Programming & Evolvable Machines, 11, 365-396. http://dx.doi.org/10.1007/s10710-010-9109-y

[taken from PRML]

  • Munir Shah, Jeremiah D. Deng, Brendon J. Woodford: A Self-adaptive CodeBook (SACB) model for real-time background subtraction. Image Vision Comput. 38: 52-64 (2015) DOI
  • Yuwei Xu, Jeremiah D. Deng, Mariusz Nowostawski, Martin K. Purvis: Optimized routing for video streaming in multi-hop wireless networks using analytical capacity estimation. J. Comput. Syst. Sci. 81(1): 145-157 (2015) [A*] DOI
  • Femi A. Aderohunmu, Davide Brunelli, Jeremiah D. Deng, Martin K. Purvis: A data acquisition protocol for a reactive wireless sensor network monitoring application, MDPI Sensors 15: 10221-10254 (2015)DOI
  • Munir Shah, Jeremiah D. Deng, Brendon J. Woodford: Video background modeling: recent approaches, issues and our proposed techniques. Mach. Vis. Appl. 25(5): 1105-1119 (2014) DOI
  • Xianbin Gu, Jeremiah D. Deng, Martin K. Purvis: Improving superpixel-based image segmentation by incorporating color covariance matrix manifolds. ICIP 2014: 4403-4406 DOI
  • Yong, S.-P., Deng, J. D., & Purvis, M. K. (2013). Wildlife video key-frame extraction based on novelty detection in semantic context. Multimedia Tools & Applications. 62(2): 359-376 (2013). DOI
  • Yong, S.-P., Deng, J. D., & Purvis, M. K. (2012). Novelty detection in wildlife scenes through semantic context modelling. Pattern Recognition, 45(9), 3439-3450. DOI [A*]
  • G. Guan, Z. Wang, S. Lu, J. D. Deng, D. D. Feng (2012). Keypoint based key frame selection, IEEE Transactions on Circuits and Systems for Video Technology, 2012. DOI
  • Suet-Peng Yong, Jeremiah D. Deng, Martin K. Purvis: Key-frame extraction of wildlife video based on semantic context modeling. IJCNN 2012: 1-8 [CORE A]
  • H. Lin, J. D. Deng, B. J. Woodford (2012). Video Manifold Modelling: Finding the Right Parameter Settings for Anomaly Detection, Proc. International Conference on Imaging and Vision Computing New Zealand, 168-173, 2012.
  • Yong, S.-P., Deng, J. D., & Purvis, M. K. (2010). Modelling semantic context for novelty detection in wildlife scenes. Proceedings of the IEEE International Conference on Multimedia and Expo (ICME), (pp. 1254-1259). IEEE. DOI