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4IZ451 – Knowledge Discovery in Databases
English
Knowledge Discovery in Databases
Czech
Dobývání znalostí z databází
ECTS credits
6
Type of course
2/2
Lecturer
Prof. Ing. Petr Berka, CSc.
Prerequisities
Not 4IZ435
Aims of the course
Knowledge Discovery in Databases (KDD) can be defined as "Non-trivial process of
identifying valid, novel, potentially useful and ultimately understandable patterns from data".
This modern branch of computer science is on the edge of database technologies, statistics
and artificial intelligence. KDD techniques can be used to solve classification, prediction,
summarization or segmentation tasks on various application areas.
Learning outcomes and competences
Students will get familiar with the methods used for knowledge discovery in databases from
both theoretical and practical point of view. The main focus of the lectures will be on
description of used data mining methods and algorithms. In practical part, the students will
work with selected data mining systems.
Upon successful completion of this course, students will be able to:
- understand the role of KDD for data analysis,
- understand the basic principles of various data mining algorithms,
- understand the basic methods of evaluation of created models,
- understand basic preprocessing operations,
- formulate and solve KDD tasks for real-world data,
- use the systems Weka, PASW Modeler and SAS Enterprise Miner.
Course contents
- The process of KDD: tasks, steps, methodologies
- The background for KDD: databases, statistics, machine learning
- Machine learning methods: decision trees, decision rules, association rules, neural nets,
bayesian methods, genetic algorithms, instance based learning
- Evaluation what has been learned
- Data preprocessing methods
- New trends: text mining, web mining
Teaching methods and workload
Type of teaching method
Daily attendance Distance form
Participation in lectures
26 h
6h
Attendance at seminars/workshops/tutorials
26 h
0h
Preparation for seminars/workshops/tutorials
26 h
45 h
Preparation of term paper
39 h
52 h
Preparation of presentation
0h
13 h
Preparation for final test
39 h
40 h
Total
156 h
156 h
Assessment methods
Requirement type Daily attendance Distance form
Term paper
30 %
30 %
Presentation
0%
10 %
Final test
70 %
60 %
Total
100 %
100 %
Recommended readings
ISBN
Title
Authors
Year
0120884070
Data Mining: Practical Machine
Learning Tools and Techniques,
Second Edition
Witten,I. - Frank,E.
2005
Han, J. - Kamber, M.
2001
1-55860-489-8 Data mining concepts and
techniques