Data Mining I - Introduction to Data Mining

 

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

Please find the appointment hours here

 

Oral examinations (3rd try only) for DM I:
January 8, 2019
January 28, 2019
Registration on the lists to be found at the Examinations Office, during
the opening hours

 

Timetable

DayTimeFrequencyPeriodRoomLecturerRemarksMax. participants
Vorlesung(V) - Lecture - Dates/Times/Location:
Tue. 13:00 bis 15:00 weekly from 17.04.2018 G44-H6 (300 Plätze) Spiliopoulou Lehrpreisträger/-in  
Übung (Ü) - Exercise - Dates/Times/Location: Group 1
Mon.09:00 bis 11:00weeklyG22A-111 (40 Plätze) Unnikrishnan  20
Übung (Ü) - Exercise - Dates/Times/Location: Group 2
Fri.11:00 bis 13:00weekly20.04.2018 to
06.07.2018
G05-210 (40 Plätze) Tutor  
Übung (Ü) - Exercise - Dates/Times/Location: Group 3
Mon.15:00 bis 17:00weeklyG22A-210 (24 Plätze) Tutor  20
Übung (Ü) - Exercise - Dates/Times/Location: Group 4
Tue.11:00 bis 13:00weeklyG22A-210 (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)
Description

 

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

Remarks

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

Export

Description Data Mining I - Introduction to Data Mining

 

Lecture

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

 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: 16.11.2018 - Contact Person:

Sie können eine Nachricht versenden an: M.Sc. Tommy Hielscher
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