Supporting Material for Data mining (MSc)

• Module code: INFO60023
• Module leader: Dr Goran Nenadic
• School resposnible: Informatics
• Credit rating: 15
  
  
• Lectures:  Tuesdays, 10-12, room C21/MSS building
• Tutorials: Tuesdays, 12-13, room J1/MSS building

Aims

The aim of this module is to provide students with knowledge and understanding of data mining and relevant applications. Students will learn about various mining concepts and techniques, including association analysis, classification, clustering and outlier detection.

Learning outcomes

On successful completion of this module, students should be able to

• Academic knowledge
  - Understand motivation and need for data mining;
  - Understand steps in the data mining process, its applicability, advantages and pitfalls;
  
  
• Intellectual skills
  - Understand the principles, methods and techniques in data mining;
  - Explain how data mining can be used to address various problems;
  - Evaluate the output produced from data mining and assess its significance;
  
  
• Subject practical skills
  - Differentiate between different data mining techniques and select an appropriate technique for a given data set and problem;
  - Apply various data mining software, methods and techniques for specific applications;
  
  
• Transferable skills
  - Apply data mining techniques to a broad range of applications;
  - Analysis, evaluation and visualisation of computational results;
  - Report writing skills.
  

Syllabus

• Introduction to data mining (overview)
• Understanding data, data pre-processing, integration and warehousing
• Data mining techniques and algorithms for
  • association analysis,
  • classification,
  • clustering,
  • outlier detection
• Evaluation and visualisation of data mining results

Reading list

• (textbook) (B) P.N. Tan, M. Steinbach, V. Kumar, Introduction to Data Mining, Addison-Wesley, 2006 (ISBN: 0-321-32136-7)
• (B) J. Han, M. Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann, 2000 (ISBN 1-55860-489-8)
• (B) I. Witten, E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, 2nd edition, Morgan Kaufmann, 2005 (ISBN: 0-12-088407-0)

Assessment

Assessment consists of 80% examination and 20% course work. The exam consists of 3 questions (selected from 5). Calculators are allowed.

Coursework description: one practical assignment (application of a data mining technique to a specific problem, and evaluation of the output), due week 10.

Resources

• Lectures (please email module leader for copies)
  • Introduction to data mining
  • Characteristics of data and data pre-processing
• Tutorials
  • WEKA
• Weka 3: data mining software
• Weka tutorial
  
  
• UK National centre for Text Mining (NaCTeM)
• www.textmining.net
  
  
• www.w3.org/
• www.w3schools.com/