Mining Patterns in Data

lingi2364  2020-2021  Louvain-la-Neuve

Mining Patterns in Data
Due to the COVID-19 crisis, the information below is subject to change, in particular that concerning the teaching mode (presential, distance or in a comodal or hybrid format).
5 credits
30.0 h + 15.0 h
Q2
Teacher(s)
Language
English
Main themes
An important task in data mining is the discovery of patterns in data. Patterns are recurring structures in data; they can provide interpretable explanations for observations in data, can help to gain a better understanding in the structure of data, can be used to build better models, and can be used to solve other computational tasks (such as the construction of database indexes or data compression). Patterns can be found in many different forms of data, including data from supermarkets, insurance companies, scientific experiments, social networks, software projects, and so on.
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.
Aims

At the end of this learning unit, the student is able to :

1 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:
  • INFO 1
  • INFO 2.1-4
  • INFO 4.2-4
  • INFO 5.5
  • INFO 6.4
Given the learning outcomes of the "Master [120] in Computer Science" program, this course contributes to the development, acquisition and evaluation of the following learning outcomes:
  • SINF 1.M4, 1.M3
  • SINF 2.1-4
  • SINF 4.2-4
  • SINF 5.5
  • SINF 6.4
Students completing this course successfully will be able to
  • Identify the most appropriate pattern mining task for a given data set ;
  • Explain the advantages and disadvantages of pattern mining algorithms in relation to the problem to be solved ;
  • Identify appropriate approaches for evaluating the quality of patterns and apply them in various situations ;
  • Determine the computational complexity of pattern mining problems;
  • Develop new pattern mining algorithms for new applications.
 

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”.
Content
  • 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
Teaching methods

Due to the COVID-19 crisis, the information in this section is particularly likely to change.

  • Lectures
  • Exercise sessions, during which exercises will be done that prepare for the exam and projets
  • 3 projets
Even though preference will be given to face-to-face lectures and exercise sessions, depending on the health situation and the number of students enrolled, other forms of teaching (online, co-modal or hybrid) may be considered.
Evaluation methods

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.
Bibliography
Charu C. Aggarwal, Jiawei Han (Eds.),  Frequent Pattern Mining, Springer 2014 (ISBN: 978-3-319-07820-5)
Chapitres de
Siegfried Nijssen, Albrecht Zimmermann and Luc De Raedt, Essentials of Pattern Mining. 
 
Faculty or entity


Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme
Sigle
Credits
Prerequisites
Aims
Master [120] in Data Science : Statistic

Master [120] in Computer Science and Engineering

Master [120] in Computer Science

Master [120] in Mathematical Engineering

Master [120] in Data Science Engineering

Master [120] in Data Science: Information Technology