Data Mining II - Advanced Topics in Data Mining


Appointment to review DM4BA, DM I and Recommenders exams:

17.04.2019: 1000 Uhr 1200hrs R130


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


 ***Update 26.10

Due to a large number of students who wished to review their exams, not all of the students could be accommodated within the time allotted for exam inspection on 24.10.2018. Students who wish to inspect their exams because they were unable to do so on 24.10 are given the opportunity to sign up for an appointment to reivew their exams. This can only be done by presenting yourself in person along with your Student ID ar G29-124 at the following hours:

Monday, 29.10.2018 between 1600-1800

Tuesday, 30.10.2018 between 1000-1200

Thursday, 01.11.2018 between 1300-1500




DayTimeFrequencyPeriodRoomLecturerRemarksMax. participants
Vorlesung(V) - Lecture - Dates/Times/Location:
Tue. 17:00 bis 19:00 weekly G22A-216 (40 Pl.) Spiliopoulou   20
Übung (Ü) - Exercise - Dates/Times/Location:
Thu.09:00 bis 11:00weeklyG22A-210 (24 Pl.)Tutor 20

Overview (from LSF)

Learning Content

In this course, we discuss advanced Data Mining methods for Data Science:

* Dealing with VELOCITY: methods for supervised, semi-supervised and unsupervised learning on data streams

* Dealing with VOLATILITY: learning and adaption on dynamic data

* Dealing with VOLUME: methods for learning on high-dimensional data

* VERACITY: incorporating expert knowledge into the learning process

From the applications' perspective, we focus on web applications and on applications from the domain of medical research.


Scientific papers (to be announced at the course)


Data Mining (recommended)

Target Group

WPF Master DKE

WPF Master Inf

WPF Master WIF

WPF Master CV

WPF Master IngInf

WPF Master Statistik

Description Data Mining II - Advanced Topics in Data Mining


Examination materials for DM II extended!
One part of the DM II exam (no more than 30%) will include questions on
a single paper, which the students read at home and compare to the
papers presented so far in the course.

A student can choose between following options:

Option "Homework Paper Assignment for In-Class Discussion": a student
participating to this option gets a paper assigned. The student reads
the paper, presents excerpts of this paper in class and receives
feedback. During the exam, this student must answer questions tailored
to that paper.

Option "Homework Default Paper": a student reads the "default" paper.
During the exam, this student must answer questions tailored to that paper.

The papers for both options, as well as questions for both options, can
be found in the last part of the DM II slideset.

Participation for the "Homework Paper Assignment for In-Class
Discussion" via
* registration during the exercise meeting of May 31, or
* via mail until June 4, 12:00 to either the class teacher or exercise tutor
Assignments will be made during that week. In exceptional cases, a paper
can also be assigned to a student who sends a mail later, but there is
no guarantee that the paper can then be presented in class.

Participation to the class meetings of DM II is not mandatory. However,
feedback for the option "Homework Paper Assignment for In-Class
Discussion" is only given while the papers are presented in the class.
Students who cannot attend the classes are advised towards option
"Homework Default Option". 


Introduction & Administrative Issues

Block 1A + 1B: Basics on streams and Stream clustering

Block 1C: Stream Classification

Block 1D: Research Advances on Stream Mining: Papers to read and instructions for the examination

Block "Active Stream Learning"


Exercise 1

Exercise 2

Exercise 3

Exercise 4

Exercise 5


Last Modification: 14.03.2019 - Contact Person:

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