This course will provide an in-depth introduction to pattern mining. After an introduction to the basics of pattern mining, it will provide an in-depth discussion of a number of advanced pattern mining techniques.
Topics that will be discussed are:
- Categories of pattern mining tasks, including pattern and pattern set mining, supervised and unsupervised pattern mining, dataset types,and pattern scoring functions;
- Algorithms for solving different pattern mining tasks;
- Data structures for making pattern mining more efficient;
- The implementation of pattern mining algorithms;
- Mathematical foundations for the different categories of pattern mining tasks;
- Complexity classes relevant to pattern mining;
- Applications of pattern mining, with a special focus on the application of pattern mining techniques in software engineering.
At the end of this learning unit, the student is able to :
Given the learning outcomes of the "Master in Computer Science and Engineering" program, this course contributes to the development, acquisition and evaluation of the following learning outcomes:
- Frequent itemset mining: algorithms, data structures;
- Constraint-based itemset mining: algorithms, data structures;
- Patterns in sequences, trees, graphs: algorithms, data structures, complexity classes;
- Pattern mining in supervised data: scoring functions, algorithms;
- Pattern set mining in supervised data: scoring functions, models (decision trees, boosting), algorithms
- Pattern set mining in unsupervised data: scoring functions (minimum description length principle, maximum entropy), algorithms
- Applications of pattern mining: software repositories, traces, log files, cheminformatics, bioinformatics, industrial applications
Due to the COVID-19 crisis, the information in this section is particularly likely to change.
- Exercise sessions, during which exercises will be done that prepare for the exam and projets
- 3 projets
Due to the COVID-19 crisis, the information in this section is particularly likely to change.An exam will be organized at the end of the semester. Depending on the health situation, this exam may be done either on campus, online, using a take-home exam, or a combination of these modalities. In case of doubt about the final grade for the exam, the teacher reserves the right to ask a student to pass a complementary oral exam.
25% for the projects + 75% for the exam; the projects only count when the grade for the exam is >= 10. If the grade for the exam is <10, the grade for the course is determined by the grade of the exam. The same conditions apply in august.
Siegfried Nijssen, Albrecht Zimmermann and Luc De Raedt, Essentials of Pattern Mining.