Instructor(s): Prof. C. Tjortjis
Teaching Hours and Credit Allocation: 30 Hours, 6 Credits
Course Assessment: Exam & Coursework
The course covers Knowledge Discovery in Databases (KDD) and Data Mining (DM) as a set of computational tools and technologies, which provide valuable assistance for business analysis and strategic business decision making. This is a hands-on course that provides an understanding of the key methods of data visualization, exploration, classification, prediction, and clustering. Students will learn how to apply various data mining techniques for solving practical problems and how to develop and use simple business analytics systems.
On completing the course, students will be able to:
- Organise and efficiently process any knowledge, either given a priori or extracted
- Understand the basic concepts of data mining
- Understand and apply various data mining approaches, including Classification, Clustering and Association Rules.
- Understand, evaluate and utilise knowledge extracted from large volumes of data.
- Introduction to Knowledge Discovery in Databases (KDD) and Data Mining (DM).
- Association Rules.
- DM Systems, Data pre-processing and Evaluation.
- J. Han and M. Kamber, Data Mining: Concepts and Techniques, 3rd ed., The Morgan Kaufmann Series in Data Management Systems, Morgan Kaufmann Publishers, 2011.
- I. Witten, E. Frank, and M. Hall, “Data Mining: Practical Machine Learning Tools and Techniques”, 3rd Ed., Morgan Kaufmann, 2011.
- J. Ledolter, Data Mining and Business Analytics with R, Wiley, 2013.
- P.N. Tan, M. Steinbach, and V. Kumar, “Introduction to Data Mining” Int’l Ed., 1/e, Pearson Higher Education, 2006.
- R. Sharda, D. Delen, E. Turban, Decision Support and Business Intelligence Systems Int’l Ed. 10/E, Pearson Higher Education, 2015.
- M.H. Dunham, “Data Mining: Introductory and Advanced Topics”, Prentice Hall, 2003.
- M.M. Gaber (ed.), Journeys to data mining: experiences from 15 renowned researchers, Springer, 2012.