Data Mining I - Introduction to Data Mining


21.11.2019: In preparation for the upcoming DMI exam in February, 2020, we're offering a tutorial on 13./20.12.2019 and 07.02.2020.

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.

18.06.2019: Exam admission overview.
Evaluation results of the third short assessment.

28.05.2019: Evaluation results of the second short assessment.

30.04.2019: Evaluation results of the first short assessment.

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).

Exam Information

  • 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.

Exercise registration

You can join any exercise. You don't need to register via LSF.






DayTimeFrequencyPeriodRoomLecturerRemarksMax. participants
Vorlesung(V) - Lecture - Dates/Times/Location:
Tue. 13:00 bis 15:00 weekly G44-H6 (300 Plätze) Spiliopoulou Lehrpreisträger/-in  
Übung (Ü) - Exercise - Dates/Times/Location: Group 1
Mon.09:00 bis 11:00weeklyfrom 08.04.2019G22A-111 (40 Plätze) Tutor  20
Übung (Ü) - Exercise - Dates/Times/Location: Group 2
Mon.15:00 bis 17:00weeklyG22A-210 (24 Plätze) Tutor  20
Übung (Ü) - Exercise - Dates/Times/Location: Group 3
Tue.11:00 bis 13:00weeklyG22A-210 (24 Plätze) Niemann  20
Übung (Ü) - Exercise - Dates/Times/Location: Group 4
Fri.09:00 bis 11:00weeklyG29-E037 (30 Plätze) Tutor  
Übung (Ü) - Exercise - Dates/Times/Location: Group 5
Fri.11:00 bis 13:00weeklyG05-210 (40 Plätze) Tutor  

Overview (from LSF)

Learning Content

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:

  • A decision maker decides which products should be offered to Internet-customers.
  • The decision maker decides, when a product will be recommended to a customer, whether the customer obtains ads and how these ads look like.
  • The decision maker may be a human or an intelligent service (as embedded in a recommendation engine)

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

  • Classification (example applications: spam recognition, distinguishing between malignant and benign tumors)
  • Clustering (example applications: customer profile learning, outlier detection)
  • Association rules (example applications: market basket analysis for cross selling and up selling, recommenders)



Pan-Ning Tan, Steinbach, Vipin Kumar. "Introduction to Data Mining", Wiley, 2004 (Auszüge, u.a. aus Kpt. 1-4, 6-8)
Selection of scientific. papers, announced during the start of the lecture

Target Group

English Master DKE

English Master DigiEng



Last Modification: 28.01.2020 - Contact Person:

Sie können eine Nachricht versenden an: M.Sc. Uli Niemann