MRes Advanced Computer Science (Data Mining)
Aims of this Pathway
To provide advanced training for students in current thinking on Knowledge Discovery in Databases (KDD) and
especially Data Mining so as to enable them to pursuer careers in commercial data mining or wishing to undertake
research in this field.
Subject based Learning Outcomes
To provide
- A broad understanding of current research themes in data mining
- Training in research project planning, analysis and interpretation of
results and the writing of research reports.
- An in depth understanding of a particular current research theme within the domain of data mining and its
parent domain of KDD.
45 Credits of taught modules
| Code | Title | Level | Credits | Semester |
|
Compulsory
|
| COMP527 | Data Mining | M | 15 | 2 |
|
30 Credits from
|
| COMP521 | Knowledge Representation | M | 15 | 1 |
| COMP522 | Privacy and Security | M | 15 | 1 |
| COMP523 | Advanced Algorithmic Techniques | M | 15 | 1 |
| COMP305 | Biocomputation | 3 | 15 | 1 |
| COMP311 | Semistructured or Web-Like Databases | 3 | 15 | 1 |
| COMP524 | Safety and Dependability | M | 15 | 2 |
| COMP525 | Reasoning About Action & Change | M | 15 | 2 |
| COMP526 | Applied Algorithmics | M | 15 | 2 |
| COMP310 | Multi-Agent Systems | 3 | 15 | 2 |
| COMP315 | Technologies for eCommerce | 3 | 15 | 2 |
| COMP317 | Semantics of Programming Languages | 3 | 15 | 2 |
| COMP318 | Advanced Web Technologies | 3 | 15 | 2 |
Indicative Research Projects
- Text mining using classification and clustering techniques.
- Key word and phrase identification for documents set categorisation.
- Isomorphism detection in transaction graph sets.
- Effective Web usage mining for eCommerce sites.
- Automated image categorisation.
- Use of Multi-Agent Systems to support KDD.
- Privacy preserving in Association Rule Mining (ARM).
- Learning to Classify Musical Styles
Please report any problems to the email address at the bottom of the page.
