COMP527

Data Mining and Visualisation

Aims

To provide an in-depth, systematic and critical understanding of some of the current research issues at the forefront of the academic research domain of data mining.

Syllabus

  1. Introduction to Data Mining, Text Mining, Data Warehousing, scope and challenges. 
  2. Classification, problem definition, basic approaches (rules, trees).
  3. Advanced solutions to the challenges of classification and regression, evaluation possibilities for classification algorithms.
  4. Input preprocessing and hybrid solutions to data mining challenges.
  5. Association Rule Mining (ARM), problem definition, current challenges and solutions.
  6. Clustering, problem definition, challenges, basic and advanced solutions.
  7. Visualisation methods and their application to data mining will be studied using several freely available visualisation tools.
  8. Web mining and information retrieval systems. Learning ranking functions.
  9. Sequntial/temporal data mining algorithms.
  10. Large scale data mining approaches and distributed learning algorithms.

Recommended Texts

None

Margaret Dunham (2003), Data Mining, Prentice Hall.

Ian Witten, Eibe Frank, Data Mining; Practical Machine Learning Tools and Techniques, Second edition (2005), Morgan Kaufmann

Christopher Bishop (2006), Pattern Recognition amd Machine Learning, Springer

Learning Outcomes

​A critical awareness of current problems and research issues in Data Mining.

A comprehensive understanding of current advanced scholarship and research in data mining and how this may contribute to the effective design and implementation of data mining applications.
The ability to consistently apply knowledge concerning current data mining research issues in an original manner and produce work which is at the forefront of current developments in the sub-discipline of data mining.
A conceptual understanding sufficient to evaluate critically current research and advanced scholarship in data mining.

Learning Strategy

Formal Lectures: Students will be expected to attend three hours of formal lectures in a typical week plus one hour supervised tutorial.

Private study: In a typical week students will be expected to devote six hours of unsupervised time to private study. The time allowed per week for private study will typically include three hours of time for reflection and consideration of lecture material and background reading, and three hours for completion of practical exercises.

Formal Lectures: Students will be expected to attend three hours of formal lectures in a typical week plus one hour supervised tutorial.

Private study: In a typical week students will be expected to devote six hours of unsupervised time to private study. The time allowed per week for private study will typically include three hours of time for reflection and consideration of lecture material and background reading, and three hours for completion of practical exercises.