Module 06-20233 (2018)

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• 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

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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