Data Mining II
Course, Master's level, 1DL460
Autumn 2023 Autumn 2023, Uppsala, 33%, On-campus, English
- Location
- Uppsala
- Pace of study
- 33%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 28 August 2023–30 October 2023
- Language of instruction
- English
- Entry requirements
-
120 credits including Data Mining I. Proficiency in English equivalent to the Swedish upper secondary course English 6.
- Selection
-
Higher education credits in science and engineering (maximum 240 credits)
- Fees
-
If you are not a citizen of a European Union (EU) or European Economic Area (EEA) country, or Switzerland, you are required to pay application and tuition fees.
- Application fee: SEK 900
- First tuition fee instalment: SEK 12,083
- Total tuition fee: SEK 12,083
- Application deadline
- 17 April 2023
- Application code
- UU-11005
Admitted or on the waiting list?
- Registration period
- 28 July 2023–4 September 2023
- Information on registration
Autumn 2023 Autumn 2023, Uppsala, 33%, On-campus, English For exchange students
- Location
- Uppsala
- Pace of study
- 33%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 28 August 2023–30 October 2023
- Language of instruction
- English
- Entry requirements
-
120 credits including Data Mining I. Proficiency in English equivalent to the Swedish upper secondary course English 6.
Admitted or on the waiting list?
- Registration period
- 28 July 2023–4 September 2023
- Information on registration
About the course
Data mining studies effective methods to find interesting patterns in large data sets. Applications can be found in biotechnology, telecom, commerce, and the internet. This course presents advanced methods in data mining covering the fields of web data mining and search engines, spatial and temporal data mining including cluster evaluation and advanced clustering, stream-based data mining, and sequential association analysis. Additional areas include advanced association analysis, methods for large-scale data mining, introduction to outlier analysis, and methods for privacy-preserving in data mining.