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Module details
CO832 Data Mining and Knowledge Discovery (15 credits)
Syllabus
- Introduction to data mining:
- The motivation for data mining.
- The nature of induction, in the context of data mining.
- Basic concepts of data mining and knowledge discovery.
- Discovery of association rules:
- Main concepts and objective of this task.
- A standard algorithm for discovering association rules.
- Classification and related prediction tasks:
- Main concepts and objective of this task.
- Classification algorithms of several paradigms, such as decision tree, rule induction, instance-based learning, and naïve Bayes.
- Discussion on the strengths and weaknesses of these kinds of algorithm.
- An overview of other data mining tasks involving prediction.
- Clustering:
- Main concepts and objective of this task.
- Classical algorithms for clustering.
- Overview of the knowledge discovery process:
- An overview of the iterative process of knowledge discovery, including not only the data mining step, but also pre-processing and post-processing steps.
- General discussion of data quality issues in the context of data mining.
- The pre-processing step of the knowledge discovery process:
- Attribute selection, discretization and attribute construction as data pre-processing tasks for data mining.
- Discussion of some algorithms for performing these pre-processing tasks.
- Post-processing of discovered knowledge and Rule Interestingness:
- Methods for refining discovered knowledge, for instance selecting the most interesting rules, out of the entire set of discovered rules.
- Review of some rule interestingness measures.
- Text mining and web mining:
- Main concepts and objective of text mining and web mining, as extensions of data mining to the more difficult problem of mining texts and web pages.
Note
This web page provides advance information about a module due to run in the coming academic year. We believe the details are accurate at the time of writing but they may be subject to change.