The student knows about different data mining methods for finding local patterns in data, their purpose and their limitations, and understands the similarities and differences between the approaches.
The student knows about tools for dealing with redundancy and pattern significance, and approaches for modelling the user's interest.
The student is able to identify techniques from the literature and adapt them to an exploratory data-analysis scenario at hand
The aim of the course is two-fold.
On one hand, the students will learn about pattern mining, from the standard frequent itemset mining task and including related techniques and problem variants which can be used for data analysis.
On the other hand, the students will train their scientific reading and writing skills. That is, the course will be an opportunity for students to practise critical reading of scientific literature, understanding and summarizing as most of the work during the course will consist in reading and reporting on selected research articles.