Data Mining I - Introduction to Data Mining
02.08.2019: The exam inspection takes place on 23.10.2019 between 09:00 and 10:00 in R128 and R130. Please register beforehand by sending a mail to Uli Niemann (first name dot last name at ovgu dot de) under specification of your name and matriculation number.
Information about the short assessment on Tuesday, 30.04.2019:
- The assessment starts at 13:15. Make sure to be seated ca. 10 min before the start to avoid delaying the procedure.
- The duation of the assessment is 30 minutes.
- The short assessment consists of 10 multiple choice questions. For each question, there is exacly one correct answer.
- Questions are based on content discussed in lecture (block classification) and exercise (sheets 1 & 2).
- Permitted materials: non-programmable calculator (transcripts and written notes are not allowed).
- The duration is 120 minutes.
- Permitted materials: non-programmable calculator
- Prohibited materials: everything else, including notes, slides, A4 cheat sheets or the like
Requirements for admission to exam
- To be admitted to the exam, you must pass 3 (written) short assessments by answering 10 out of 30 questions correctly.
- Each short assessment consists of 10 multiple choice questions.
- If you make more than 6 mistakes in a short assessment, the remaining correct answers of that short assessment will be annulled.
- Each short assessment will take place at the beginning of a lecture.
- Dates: 30.04., 28.05., 18.06.
- Questions are based on content discussed both in lecture and exercise.
- If you have failed the exam before summer term 2019, you don't need to acquire admission to exam. However, you are allowed to participate at the short assessments as practice for exam preparation.
You can join any exercise. You don't need to register via LSF.
- Introduction (revised on 09.04.19)
- Classification (revised on 09.04.19)
- Special Topics on Supervised Model Learning - Multi-Target & Multi-Label Classification -Update
- Special Topics on Supervised Model Learning - Class Imbalance & Statistical Testing
- Association Analysis
- Exercise sheet 1
- Exercise sheet 2
- Exercise sheet 3
- Exercise sheet 4
- Exercise sheet 5
- Exercise sheet 6
- Exercise sheet 7
- Exercise sheet 8
- Exercise sheet 9
- Exercise sheet 10
- Exercise sheet 11
|Vorlesung(V) - Lecture - Dates/Times/Location:|
|Tue.||13:00 bis 15:00||weekly||G44-H6 (300 Plätze)||Spiliopoulou|
|Übung (Ü) - Exercise - Dates/Times/Location: Group 1|
|Mon.||09:00 bis 11:00||weekly||from 08.04.2019||G22A-111 (40 Plätze)||Tutor||20|
|Übung (Ü) - Exercise - Dates/Times/Location: Group 2|
|Mon.||15:00 bis 17:00||weekly||G22A-210 (24 Plätze)||Tutor||20|
|Übung (Ü) - Exercise - Dates/Times/Location: Group 3|
|Tue.||11:00 bis 13:00||weekly||G22A-210 (24 Plätze)||Niemann||20|
|Übung (Ü) - Exercise - Dates/Times/Location: Group 4|
|Fri.||09:00 bis 11:00||weekly||G29-E037 (30 Plätze)||Tutor|
|Übung (Ü) - Exercise - Dates/Times/Location: Group 5|
|Fri.||11:00 bis 13:00||weekly||G05-210 (40 Plätze)||Tutor|
Overview (from LSF)
Data mining is a family of methods used e.g. in recommenders and in decision support systems for prediction, for customer profiling, for classification and outlier detection. For example:
For such decisions, the decision maker uses models that captures the preferences, price sensitivity and attitudes of customers, the behaviour of customers and the similarity among customers. In this bachelor course, we discuss methods for deriving models from data. In particular, we discuss
Pan-Ning Tan, Steinbach, Vipin Kumar. "Introduction to Data Mining", Wiley, 2004 (Auszüge, u.a. aus Kpt. 1-4, 6-8)
English Master DKE
English Master DigiEng