Module 06-20233 (2011)

• 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

• 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

2 hrs of lectures per week

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

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