Data Mining I - Introduction to Data Mining

Timetable

DayTimeFrequencyPeriodRoomLecturerRemarksMax. participants
Vorlesung(V) - Lecture - Dates/Times/Location:
Mon. 13:00 bis 15:00 weekly 08.04.2024 to
08.07.2024
G26-H1 (572 Pl.) Spiliopoulou   250
Mon. 13:00 bis 15:00 weekly 08.04.2024 to
08.07.2024
G40B-231 (91 Pl.)    
Übung (Ü) - Exercise - Dates/Times/Location: Group 1
Thu.11:00 bis 13:00weekly11.04.2024 to
11.07.2024
G22A-217 (40 Pl.) Beyer  30
Übung (Ü) - Exercise - Dates/Times/Location: Group 2
Thu.11:00 bis 13:00weekly11.04.2024 to
11.07.2024
G22A-122 (40 Pl.) Puga  30
Übung (Ü) - Exercise - Dates/Times/Location: Group 3
Wed.13:00 bis 15:00weekly10.04.2024 to
10.07.2024
G22A-209 (40 Pl.) Tutor ,
Tutor-Data Mining I - Introduction to Data Mining
 30
Übung (Ü) - Exercise - Dates/Times/Location: Group 4
Thu.11:00 bis 13:00weekly11.04.2024 to
11.07.2024
G22A-209 (40 Pl.) Tutor ,
TUTOR-DM4BA
 30

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)
Comments

Please use the LSF application function to register for the course.

Course materials will be provided on Moodle, see hyperlink.

Description

(You only need to enroll for the lecture.)

Literature

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

Certificates

Exam admission requires a successful completion of multiple short written assessments. Details, including number and dates of the short assessments, minimum number of points to pass and minimum number of assessements to pass, will be announced at the beginning of the semester.

Target Group

English Master DKE

English Master DigiEng

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Last Modification: 27.08.2024 - Contact Person: