Data Mining II - Advanced Topics in Data Mining
News:
Exercise 8 will be conducted on June 27, 2019
Timetable
| Day | Time | Frequency | Period | Room | Lecturer | Remarks | Max. participants |
|---|---|---|---|---|---|---|---|
| Vorlesung(V) - Lecture - Dates/Times/Location: | |||||||
| Tue. | 15:00 bis 17:00 | weekly | G29-K059 (24 Pl.) | Spiliopoulou | |||
| Übung (Ü) - Exercise - Dates/Times/Location: | |||||||
| Thu. | 09:00 bis 11:00 | weekly | G22A-210 (24 Pl.) | Tutor | 20 | ||
| Thu. | 17:00 bis 19:00 | weekly | G29-K059 (24 Pl.) | Unnikrishnan | |||
Overview (from LSF)
| Learning Content | In this course, we discuss advanced Data Mining methods for Data Science: * Dealing with VELOCITY: methods for supervised, semi-supervised and unsupervised learning on data streams * Dealing with VOLATILITY: learning and adaption on dynamic data * Dealing with VOLUME: methods for learning on high-dimensional data * VERACITY: incorporating expert knowledge into the learning process From the applications' perspective, we focus on web applications and on applications from the domain of medical research. |
|---|---|
| Literature | Scientific papers (to be announced at the course) |
| Prerequisites | Data Mining (recommended) |
| Target Group | WPF Master DKE WPF Master Inf WPF Master WIF WPF Master CV WPF Master IngInf WPF Master Statistik |
| Description | Data Mining II - Advanced Topics in Data Mining |
Lecture
- Slides: Administrative
- Slides: Block 1A - Stream Basics
- Slides: Block 1B - Stream Clustering
- Slides: Block: Learning on Streams - 1C - Stream classification
- Slides: [Homework Option] Medical Mining Papers -Update
- Slides: [Default Option] Outcome-Oriented Business Process Monitoring
Homework option for DM II: Questions are here and here.
Exercise
- Exercise 1
- Exercise 2 (Example solutions from Tirtha Chanda)
- Exercise 3
- Exercise 4
- Exercise 5
- Exercise 6 [Solutions]
- Exercise 7
- Exercise 8 will be conducted on June 27, 2019