Module 06-20233 (2013)

• 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

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

2 hrs of lectures per week

Contact Hours: 23

Sessional: 1.5 hr examination (80%), continuous assessment (20%)

Supplementary (where allowed): 1.5 hr examination (80%) with the continuous assessment mark carried forward (20%)

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