Course details

Knowledge Discovery in Databases

ZZN Acad. year 2020/2021 Winter semester 5 credits

Data warehouses. Data mining techniques  association rules, classification and prediction, clustering. Mining unconventional data - data streams, time series and sequences, graphs, spatial and spatio-temporal data, multimedia. Text and web mining. Working-out a data mining project by means of an available data mining tool.

Guarantor

Deputy Guarantor

Language of instruction

Czech

Completion

Credit+Examination (written)

Time span

39 hrs lectures, 13 hrs projects

Assessment points

51 exam, 15 half-term test, 34 projects

Department

Lecturer

Instructor

Subject specific learning outcomes and competences

  • Students get a broad, yet in-depth overview of the field of data mining and knowledge discovery.
  • They are able both to use and to develop knowledge discovery tools.

Generic learning outcomes and competences

  • Student learns terminology in Czech and English.
  • Student gains experience in solving projects in a small team.
  • Student improves his ability to present and defend the results of projects.

Learning objectives

To familiarize students with the methods and algorithms of data modelling for knowledge discovery from it.

Why is the course taught

Due to the increasing amounts of data currently stored in databases and other data sources, it is necessary to discover some new knowledge, which is not possible to obtain with use of querying the data. Therefore, in connection with the knowledge and skills from the subject UPA  related to the data mining process and to the data preparation before its modelling, it is necessary to get acquainted with the data modelling methods and algorithms. They are based on methods and techniques from various areas, such as statistics and machine learning. 

Prerequisite kwnowledge and skills

  • Knowledge of the basic steps of the data mining process and methods of data preparation for the step of data modelling (discussed in the subject UPA - Data Storage and Preparation).
  • Basic knowledge of probability and statistics.
  • Knowledge of database technology at a bachelor subject level. 

Study literature

  • Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Third Edition. Morgan Kaufmann Publishers, 2012, 703 p., ISBN 978-0-12-381479-1.
  • Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Second Edition. Elsevier Inc., 2006, 770 p., ISBN 1-55860-901-3.
  • Skiena, S.S.: The Data Science Design Manual. Springer, 2017, 445 p. ISBN 978-3-319-55443-3.
  • Bishop, C.M: Pattern Recognition and Machine Learning. Springer, 2006, 738 p. ISBN 0387310738.     

Fundamental literature

  • Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Third Edition. Morgan Kaufmann Publishers, 2012, 703 p., ISBN 978-0-12-381479-1.
  • Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Second Edition. Elsevier Inc., 2006, 770 p., ISBN 1-55860-901-3.

Syllabus of lectures

  1. Data Warehouse and OLAP Technology for knowledge discovery.
  2. Mining frequent patterns and associations - basic concepts, efficient and scalable frequent itemset mining methods.
  3. Multi-level association rules, association mining and correlation analysis, constraint-based association rules.
  4. Predictive modelling - basic concepts, classification methods - decision tree, Bayesian classification, rule-based classification.
  5. Classification by means of neural networks, SVM classifier, Random forests.
  6. Other classification and regression methods. Evaluation of quality of classification and regression.
    Cluster analysis - basic concepts, types of data in cluster analysis.
  7. Partitioning-based and hierarchical clustering. Other clustering methods. Evaluation of quality of clustering.
  8. Outlier analysis. Mining in biological data.
  9. Introduction to mining data stream and time-series.
  10. Introduction to mining in sequences, graphs, spatio-temporal data, moving object data and multimédia data. 
  11. Text mining.
  12. Mining the Web. Process mining.
  13. Introduction to big data analytics.

Syllabus - others, projects and individual work of students

  • Working-out a data mining project by means of an available data mining tool.

Progress assessment

A mid-term test, formulation of a data mining task, presentation of the project.

Controlled instruction

  • Mid-term written exam, there is no resit, excused absences are solved by the guarantor.
  • The formulation of the data mining task in the prescribed term, excused absences are solved by the assistent.
  • The presentation of the project results in the prescribed term, excused absences are solved by the assistent.
  • Final exam, The minimal number of points which can be obtained from the final exam is 20. Otherwise, no points will be assigned to the student. excused absences are solved by the guarantor.

Exam prerequisites

Duty credit consists of working-out the project, defending project results and of obtaining at least 24 points for activities during semester.

Schedule

DayTypeWeeksRoomStartEndLect.grpGroupsInfo
Tuelecturelectures D0207 12:0014:50 1MIT 2MIT NBIO - NISY NISY xx
Tuelecture2., 3., 4., 5., 6., 7., 8. of lectures D0207v 12:0014:50ZP, MST, bez YT, bez projekce

Course inclusion in study plans

Back to top