Otto-von-Guericke-Universität Magdeburg


Data Mining I - Introduction to Data Mining


Information about the inspection of the exam paper is available here.


DayTimeFrequencyPeriodRoomLecturerRemarksMax. participants
Vorlesung(V) - Lecture - Dates/Times/Location:
Tue. 13:00 bis 15:00 weekly G22A-203 (40 Plätze) Spiliopoulou Lehrpreisträger/-in   40
Übung (Ü) - Exercise - Dates/Times/Location: Group 1
Mon.09:00 bis 11:00weeklyG22A-111 (40 Plätze) Tutor  20
Übung (Ü) - Exercise - Dates/Times/Location: Group 2
Thu.15:00 bis 17:00weeklyG22A-113 (24 Plätze) Tutor  20

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


Description Data Mining I - Introduction to Data Mining


Block 0 - Introduction

Block 1 - Classification - Part 1 (UPDATE 02.05.2017)

Block 1 - Classification - Part 2

Block 2 - Clustering

Block 3 - Assoziationsregeln


Exercise 1

Exercise 2

Exercise 3

Exercise 4

Exercise 5

Exercise 6

Exercise 7

Exercise 8

Exercise 9


Letzte Änderung: 12.09.2017 - Contact Person: M.Sc. Tommy Hielscher