Data mining is about the extraction of non-trivial, implicit, previously unknown and potentially useful principles, patterns or knowledge from massive amount of data. This course introduces the foundation of data mining techniques, including basic concepts of data representation, new software stack for processing massive data such as MapReduce and Spark, and popular data mining tasks like mining frequent itemsets, nearest neighbor search, clustering analysis and graph mining. Students will also learn how data mining techniques are used in real-world applications such as online advertising and recommender systems.

Note: To pass the course, apart from obtaining a minimum of 40% in the overall mark, a student must also obtain a minimum mark of 30% in both continuous assessment and examination components.