Data Mining I - Introduction to Data Mining


Appointment to review DM4BA, DM I and Recommenders exams:

17.04.2019: 1000 Uhr 1200hrs R130

***For the students who signed up for a second chance to inspect their examination papers***

Please find the appointment hours here


3rd try for DM I
     Wednesday, June 12
  Slots between 9:30 and 11:30
  Slots between 15:00 and 16:00
  Please register via Examinations Office



DayTimeFrequencyPeriodRoomLecturerRemarksMax. participants
Vorlesung(V) - Lecture - Dates/Times/Location:
Tue. 13:00 bis 15:00 weekly from 17.04.2018 G44-H6 (300 Pl.) Spiliopoulou  
Übung (Ü) - Exercise - Dates/Times/Location: Group 1
Mon.09:00 bis 11:00weeklyG22A-111 (40 Pl.) Unnikrishnan  20
Übung (Ü) - Exercise - Dates/Times/Location: Group 2
Fri.11:00 bis 13:00weekly20.04.2018 to
G05-210 (40 Pl.)Tutor 
Übung (Ü) - Exercise - Dates/Times/Location: Group 3
Mon.15:00 bis 17:00weeklyG22A-210 (24 Pl.)Tutor 20
Übung (Ü) - Exercise - Dates/Times/Location: Group 4
Tue.11:00 bis 13:00weeklyG22A-210 (24 Pl.)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


Attention, new room! The event "Data Mining I - Introduction to Data Mining" takes place in G44-H6.

Target Group

English Master DKE

English Master DigiEng


Description Data Mining I - Introduction to Data Mining



Introduction and Administratives

Block "Classification" (with modifications)

Block "Clustering"

Block "Frequent Itemset Discovery for Association Rule Learning and Classification Rule Learning"

[Paper] X-means: Extending k-means with efficient estimation of the number of clusters.

Examination Announcements


 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


Last Modification: 14.03.2019 - Contact Person:

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