Data Mining

Graduate

This course will introduce basic data mining techniques such as preprocessing (e.g., aggregation, sampling and dimensionality reduction), pattern mining, classification (e.g., decision trees, rules), prediction modelling (e.g., Bayesian networks, nearest neighbours), cluster analysis, and outlier detection before covering more advanced data mining techniques such as advanced clustering (e.g., graph-based and fuzzy clustering), infrequent patterns, machine learning and avoiding false discoveries.