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:
Given the learning outcomes of the "Master  in Computer Science" program, this course contributes to the development, acquisition and evaluation of the following learning outcomes:
Students completing this course successfully will be able to
The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
- 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
- 3 exercises
Siegfried Nijssen, Albrecht Zimmermann and Luc De Raedt, Essentials of Pattern Mining.