Faculty of Information Technology, BUT

Course details

Knowledge Discovery in Databases

ZZN Acad. year 2019/2020 Winter semester 5 credits

Basic concepts concerning knowledge discovery in data, relation of knowledge discovery and data mining. Data sources for knowledge discovery. Principles and techniques of data preprocessing for mining. Systems for knowledge discovery in data, data mining query languages. 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 knowledge discovery in data sources, to explain useful knowledge types and the steps of the knowledge discovery process, and to familiarize them with techniques, algorithms and tools used in the process.

Why is the course taught

Due to the increasing amounts of data currently stored in databases, it is necessary to discover some new knowledge, which is not possible to obtain with use of querying the data. Therefore, it is necessary to become acquainted with the whole process of knowledge discovery, which includes data pre-processing and data mining algorithms, where methods from various areas, such as statistics and machine learning, is used. 

Prerequisite kwnowledge and skills

  • 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.
  • 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. Introduction - motivation, fundamental concepts, data source and knowledge types.
  2. Data Preparation - characteristics of data.
  3. Data Preparation - methods.
  4. Data Warehouse and OLAP Technology for knowledge discovery.
  5. Mining frequent patterns and associations - basic concepts, efficient and scalable frequent itemset mining methods.
  6. Multi-level association rules, association mining and correlation analysis, constraint-based association rules.
  7. Classification and prediction - basic concepts, decision tree, Bayesian classification, rule-based classification.
  8. Classification by means of neural networks, SVM classifier, other classification methods, prediction.
  9. Cluster analysis - basic concepts, types of data in cluster analysis, partitioning and hierarchical methods.
  10. Other clustering methods. Mining in biological data.
  11. Introduction to mining data stream, time-series and sequence data.
  12. Introduction to mining in graphs, spatio-temporal data, moving object data and multimédia data. 
  13. Text mining, mining the Web.

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

A mid-term test, formulation of a data mining task, presentation of the project. The minimal number of points which can be obtained from the final exam is 20. Otherwise, no points will be assigned to the student.

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 MBI MIN xx

Course inclusion in study plans

Back to top