School of Computer Science

Module 06-20233 (2012)

Intelligent Data Analysis (Extended)

Level 4/M

Peter Tino Semester 2 10 credits
Co-ordinator: Peter Tino
Reviewer: Russell Beale

The Module Description is a strict subset of this Syllabus Page.

Aims

The aims of this module are to:

  • introduce some of the fundamental techniques and principles of statistical pattern analysis and data mining
  • investigate some common text and high-dimensional data mining algorithms and their applications
  • present the fields of data mining and pattern analysis in the larger context of learning systems

Learning Outcomes

On successful completion of this module, the student should be able to:

  • explain principles and algorithms for dimensionality reduction and clustering of vectorial data
  • explain principles and techniques for mining textual data
  • demonstrate understanding of the principles of efficient web-mining algorithms
  • demonstrate understanding of broader issues of learning and generalisation in pattern analysis and data mining systems
  • identify, justify and apply suitable dimensionality reduction and visualisation techniques to mine real-world vectorial data

Taught with


Teaching methods

2 hrs of lectures per week


Assessment

  • Sessional: 1.5 hr examination (80%), continuous assessment (20%).
  • Supplementary: By examination only.

Detailed Syllabus

  1. Overview. Various forms of pattern analysis and data mining
  2. Basics of vector and metric spaces, probability theory and statistics
  3. Analysing vectorial data
    • Dimensionality reduction techniques
    • Clustering techniques
    • Classification and regression techniques
  4. Analysing structured data
    • Mining textual data
    • Other structured data types
  5. Searching the web

Programmes containing this module