Data Mining Techniques
Date:
23/02/2017 - 24/02/2017
Organised by:
Lancaster University
Presenter:
Professor Brian Francis
Level:
Intermediate (some prior knowledge)
Contact:
Angela Mercer, email: a.j.mercer@lancaster.ac.uk, Tel: 593064
Map:
View in Google Maps (LA1 4YF)
Venue:
Postgraduate Statistics Centre, c/o Mathematics and Statistics Department, Faculty of Science and Technology, Lancaster, LA1 4YF, UK
Description:
The main aim of data mining is to extract knowledge, or information, which is stored in very large databases. This module covers many of the concepts that are fundamental to understanding and successfully applying data mining methods. Statistical concepts are discussed without mathematically complex formulation. Practical sessions will use the latest versions of standard software rather than data mining – the emphasis of the module is on techniques rather than data mining software.
The module will include the following:
1. formulating research objectives that can be translated into suitable analytical methods;
2. data structure and organisation;
3. model comparison and assessment;
4. data splitting;
5. assessing and interpreting predictive models;
6. introduction to variable selection;
7. benefits and drawbacks of neural networks;
8. examining the benefits and drawbacks of regression trees;
9. cluster analysis and latent class analysis;
10. bootstrap and cross-validation.
Cost:
External from industry/commerce £540; External from academic institution/public sector/charity staff £460; External postgraduate students £300.
Website and registration:
http://www.lancaster.ac.uk/maths/postgraduate/short-courses-and-cpd/
Region:
North West
Keywords:
Data Mining, Data Mining
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