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registrar@umat.edu.gh

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Dr. Vincent M. Nofong

vnofong@umat.edu.gh


Position

Training Officer, Computer Science and Engineering Department

Department

Computer Science and Engineering

Research Interest

Data mining

Pattern mining

Trend Prediction

Classification

Knowledge Representation

Bioinformatics

Concept Drift




Vincent M. Nofong, Ph.D is a lecturer and researcher working on data mining techniques and their applications.

2016
Ph.D. Computer and Information Science
University of South Australia
2010
BSc. (Hons) Electrical and Electronic Engineering
University of Mines and Technology
2012--2016
University of South Australia
2006--2010
University of Mines and Technology
2019--Now
ACM Special Interest Group on Knowledge Discovery and Data Mining
2016--Now
Australian Computer Society
2015--Now
Association for Computing Machinery
2020
Erasmus+ Mobility Program, Project No. 2019-1-HR01-KA107-060762, 2020
2016
Best Student Paper, 39th Australasian Computer Science Conference
2012
University of South Australia President's Scholarship
2012
UMaT/GETFund Postgraduate Scholarship
2016--Now
Lecturer
  1. Nofong, V. M. and Abdel-Fatao, H. (2021), " Towards Predicting the Occurrence Times of Emerging and Decaying Patterns.", Data Science and Pattern Recognition., 5 (1), 16-26

  2. Afriyie, M. K, Nofong, V. M., Wondoh, J. and Abdel-Fatao, H. (2021), "Efficient Mining of Non-Redundant Periodic Frequent Patterns.", Vietnam Journal of Computer Science., 8 (4), 1-15

  3. Nofong, V. M. and Wondoh, J. (2019), "Towards Fast and Memory Efficient Mining of Periodic Frequent Patterns.", Journal of Information and Telecommunication., 3 (4), 480-493

  4. Nofong, V. M. (2016), "Discovering Productive Periodic Frequent Patterns in Transactional Databases.", Annals of Data Science., 3 (3), 235-249

  1. Afriyie, M. K, Nofong, V. M., Wondoh, J., and Abdel-Fatao, H. (2020), " Mining Non-redundant Periodic Frequent Patterns", Intelligent Information and Database Systems. LNCS, vol 12033., 321-331
  2. Nofong, V. M. (2018), " Fast and Memory Efficient Mining of Periodic Frequent Patterns.", Modern Approaches for Intelligent Information and Database Systems. SCI, vol 769, 223-232
  3. Nofong, V. M. (2016), "EDTrend: A Methodology for Trend Prediction with Emerging and Decaying Patterns.", Australasian Computer Science Week Multiconference, 1-8
  4. Nofong, V. M., Liu J. and Li J. (2015), "Efficient Mining of Non-derivable Emerging Patterns", Databases Theory and Applications, LNCS, vol. 9093, 244–256
  5. Nofong, V. M. (2015), "Mining Productive Emerging Patterns and Their Application in Trend Prediction"", 13th Australasian Data Mining Conference, CRPIT vol. 168, 109-117
  6. Nofong, V. M. (2015), "Discovering Productive Periodic Frequent Patterns in Transactional Databases", Data Science, LNCS, vol. 9208, 141-150
  7. Nofong, V. M., Liu J. and Li J. (2014), "A Study on the Applications of Emerging Sequential Patterns", Databases Theory and Applications, LNCS, vol. 8506, 62–73
Nofong, V. M., Abdel-Fatao H., Afriyie, M. K. and Wondoh, J.(2021), Discovering Self- Reliant Periodic Frequent Patterns Periodic Pattern Mining: Theory, Algorithms and Applications, 222
Data Mining and Data Warehousing
Programming with Java
Data Structures and Algorithms
Operating Systems
2020--
Reviewer, Applied Intelligence
2019--
Reviewer, Knowledge-Based Systems
2017--
Reviewer, Decision Support Systems
2016--
Reviewer, Asian Conference on Intelligent Information and Database Systems
2013--
Reviewer, Advances in Social Network Analysis and Mining
2012--
Reviewer, Advances in Artificial Intelligence