Advanced Data Mining
Mine data and inspect the underlying structure of market research and social science. Navigate dimensional analysis mapping, segmentation and preference techniques. Map suitable classification methods and report data mining results.
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Subjects may require attendance
- 26 Aug 2019
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Students who successfully complete this subject will be able to:
- Formulate an understanding of the basic theory and principles of data mining
- Assess prediction and classification methods such as regression, neural networks and decision trees, understanding how to choose between these methods
- Evaluate unsupervised data mining methods and determine when these methods should be combined with supervised methods
- Design ontologies based on text and creative visualisations of textual relationships
- Report on the results of specific data mining projects.
- Introduction to data mining and a data mining package
- Linear Models
- Classification and regression trees
- Random forests and boosting
- Neural networks for classification and prediction
- Self-organising maps
- Cluster analysis
- Memory Based Reasoning
- Support Vector Machines
- Text Mining
- Discussion forum/Discussion Board
- Online Quizzes/Tests
- Online assignment submission
- Podcasting/Lecture capture
- Standard Media
- Web links
- Welcome letter
- Printable format materials
You cannot enrol in this subject if you have successfully completed any of the following subject(s) because they are considered academically equivalent:
You must have successfully completed the following subject(s) before starting this subject:
SWI-STA70002-Multivariate Statistics , or SWI-HMS780
- OtherDetails -
You will require access to a recent version of SPSS and the latest version of SAS Enterprise Miner. Note: SAS will only run on a Windows platform.
Please note: this subject was previously titled Statistical Marketing Tools
This subject introduces the principles and techniques used by data miners. Data mining is used to add value to large collections of data, delivering discoveries that continue to revolutionise lives in our data-rich but knowledge-hungry world.
Please note: assessment values are indicative only, details will be advised at the start of the subject.
- Invigilated Exam (50%)
- Quizzes — Online (10%)
- Assignments — 2 (40%)
Click on the titles of the listed books below to find out more:
Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery