Data Mining & Knowledge Discovery Coursework, March 2012
Data Mining Techniques and Applications
Data mining encompasses a number of different technical approaches, such as:
Association Rules/Link Analysis, Clustering, Classification, and Deviation Detection/outlier Analysis. Choose two techniques and provide the following:
1. Technical Appraisal
Provide a critical analysis and technical appraisal of the chosen techniques. Include a detailed description of the technique and the methods involved. This should be taken beyond the scope of that covered in class. You should also include details of current and future developments and potential uses.
Produce the report (minimum 6 pages) in the form of either a research paper or article in a professional journal or online technical website.
1. Case Studies
Describe two case studies for each of the techniques you have chosen. Choose any type of application you find interesting which is a good illustration of the techniques being used effectively. You should provide a critical appraisal/analysis of each case study.
The format should be suitable for online publication. Indicate the intended audience and ensure information is presented in a fashion suitable for targeted readers.
Technical Description (20), Developments (10), analysis (10), applications (10), Conclusions (16) 65
Description (10) appropriateness (10), appraisal/Analysis (10) 30
Submitted through the Digital Dropbox on Blackboard.
You are encouraged to submit before this date. In accordance with current University rules, late submission within the first week will be penalised by 10%, but will not be accepted thereafter. The University rules on plagiarism are quite clear. Plagiarism will automatically be marked at zero. If you make use of freely available images from the web, please include copyright information as required. Web references should state the full URL, title of site/page, and date & time accessed.
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