| Period(s) | I | II | III | IV |
| 2012–2013 | X | X | ||
| 2013–2014 | X | X | ||
| 2014–2015 | X | X |
Learning outcomes
After completing the course a student
• knows the phases of the process of knowledge discovery and understands its nature
• knows different types of data mining tasks and methods and understands their requirements and limitations
• is able to choose and apply appropriate preprocessing and data mining methods to a given data set and problem and evaluate mined results
• is able to act in practical knowledge discovery processes.
Contents
Steps in the process of knowledge discovery: data preprocessing, data mining, post-processing and knowledge utilisation. Preprocessing: data cleaning, integration, transformation and reduction. Data mining methods: association analysis, classification and clustering. Post-processing: knowledge evaluation, interpretation and visualisation. Knowledge discovery and data management. Examples of knowledge discovery systems and practical application areas. Possibly other selected topics in knowledge discovery.
Teaching methods
| Teaching method | Contact | Online |
| Lectures | 40 h | 0 h |
| Exercises | 20 h | 0 h |
Teaching language
FinnishModes of study
- Degree Programme Students
- Other Students
- Open University Students
- Doctoral Students
- Exchange Students
The course can be taken in English by doing weekly exercises, written examination, and course assignment. Teaching in lectures and on weekly exercise sessions will be in Finnish only, so this course requires a lot of self-directed learning.
Evaluation
Numeric 1-5.Belongs to following study modules
2014–2015
