Knowledge Discovery in Databases II (SoSe 2018)
News
2nd Exam: 18.10.2018, 14 - 16 Uhr, room B 101 (main building)
Organisation
- Umfang: 3+2 Semesterwochenstunden
- Lecture: Prof. Dr. Peer Kröger
- Assistant: Yifeng Lu
- Required: Lecture "Knowledge Discovery in Databases I" or equivalent
- ECTS: 6
Time and Locations
| Component | When | Where | Starts at |
| Lecture | Wed, 9,00 - 12,00 h | Room B U101 (Oettingenstr. 67) | 11.04.2018 |
| Tutorial 1 | Mon, 14,00 - 16,00 h | Room M 001 (HGB) | 16.04.2018 |
| Tutorial 2 | Mon, 16,00 - 18,00 h | Room M 001 (HGB) | 16.04.2018 |
| Tutorial 3 | Tue, 14,00 - 16,00 h | Room A 120 (HGB) | 17.04.2018 |
| Tutorial 4 | Tue, 16,00 - 18,00 h | Room A 120 (HGB) | 17.04.2018 |
Time and Locations
| Lecture | Tutorial | |||
|---|---|---|---|---|
| Date | Topic | Date | Exercise | Material |
| 11.04.18 | Introduction | |||
| 18.04.18 | High Dimensional Data (Intro, Curse of Dimensionality | 23/24.04.18 | ex1 | optics-data.zip ArffGen.zip sol1.pdf |
| 25.04.18 | High Dimensional Data (Feature Selection) | ex2 | ex2.zip feature_func | |
| 02.05.18 | High Dimensional Data (Dimensionality Reduction) | 07/08.05.18 | ex3 | |
| 09.05.18 | Guest Lecture: Dr. Gerhard Rolletschek, Glanos GmbH | 14/15.05.18 | ex4 | kpca.csv |
| 16.05.18 | High Dimensional Data (Clustering) | |||
| 23.05.18 | High Dimensional Data (Clustering - cont.) Achtung: Beginn: 10:15 Uhr |
28/29.05.18 | ex5 | |
| 30.05.18 | Guest Lecture: Process Mining | |||
| 06.06.18 | Data Streams (Clustering) | 11/12.06.18 | ex6 | ex6.1_solution |
| 13.06.18 | Data Streams (Classification) | 18/19.06.18 | ex7 | |
| 20.06.18 | Sequence Data/Time Series | 25/26.06.18 | ex8 | |
| 27.06.18 | Time Series/Spatio-temporal Data | 02/03.07.18 | ex9 | |
| 04.07.18 | Graph Data | 09/10.07.18 | Recap | |
Content
In many modern application areas, data scientists face challenges which go beyond the basic techniques being introduced in the basic module Knowledge Discovery in Databases I. The module Knowledge Discovery in Databases II covers advanced techniques to handle large data volumes, volatile data streams, complex object descriptions and linked data. These topics are also known as the three major challenges (Volume, Velocity, Variety) in Big Data Analysis. The module is directed at master students being interested in developing and designing knowledge discovery processes for various types of applications. This includes the development of new data mining and data preprocessing methods as well as the ability to select the best suited established approach for a given practical challenge.