Learning outcomes

After completing the course (5 ECTS) a student
- knows the phases of the process of knowledge discovery and understands its nature
- knows basic data prepocessing, data mining and postprocessing tasks and methods
- is able to apply these methods in practical knowledge discovery tasks

After completing the course (10 ECTS) a student
- in addition to the above-mentioned outcomes
- knows and is able to apply advanced methods used in the process of knowledge discovery
- knows data management issues concerning knowledge discovery

Contents

Steps in the process of knowledge discovery: data preprocessing, data mining, postprocessing and knowledge utilisation.
Preprocessing: data cleaning, integration, transformation and reduction.
Data mining tasks and methods: association analysis, classification and clustering.
Postprocessing: knowledge evaluation, interpretation and visualisation.
Examples of knowledge discovery systems and practical application areas.
Knowledge discovery and data management.
Possibly other selected topics in knowledge discovery.

Teaching methods

Teaching language

Evaluation

Belongs to following study modules

2018–2019

Teaching

Archived Teaching Schedule. Please refer to current Teaching Shedule.

TIETS31 Knowledge Discovery