Sample DB and algo codebase for CSEN3123 Data Mining and Knowledge Discovery
This repository is meant to help students visualise and practically apply the algorithms taught in the course. It's created and maintained by a student, thus corrections and improvements are highly appreciated.
Below are info provided in the syllabus course structure (updated 2022)
After completion of the course, students will be able to:
- CSEN3132.1. Learn and understand basic knowledge of data mining and related models.
- CSEN3132.2. Understand and describe data mining algorithms.
- CSEN3132.3. Understand and apply Data mining algorithms.
- CSEN3132.4. Suggest appropriate solutions to data mining problems.
- CSEN3132.5. Analyze data mining algorithms and techniques.
- CSEN3132.6. Perform experiments in Data mining and knowledge discovery using real-world data.
- Data Mining Concepts and Techniques, 3rd, Edition, J. Han and M. Kamber, Morgan Kaufmann Publishers, July 2011.
- Introduction to Data Mining, P. N. Tan, M. Steinbach and V. Kumar, Pearson Publishers.
- Pattern Recognition and Machine Learning, First Edition, C. Bishop, Springer, 2006.
- Neural Networks and Learning Machines, Third Edition, S. Haykin, PHI Learning, 2009.
- Pattern Classification, Second Edition, R. Duda, P. Hart and D. Stock, Wiley-Interscience, 2000