DM838: Data Mining (10 ECTS)

STADS: 15014601

Level
Master's level course

Teaching period
The course is offered in the spring semester.

Teacher responsible
No responsible teachers found, contact the department if necessary

Timetable
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Comment:
Underviser: Fabio Vandin. Ubegrænset deltagerantal. 3. + 4. kvartal.

Prerequisites:
None

Academic preconditions:
The content of DM507 Algorithms and Data Structures, DM535 Discrete Methods for Computer Science, and DM536 Introduction to programming should be known.

Course introduction
During the last few years, the advances in data acquisition technologies have led to the exponential grown of data that is routinely accumulated by many organizations (tech. companies, scientific labs, etc.). This has driven the interest in data mining, that is the extraction of hidden and useful information from a data set, due to the potential impact of patterns discovered from these data sets.

This course presents the most common techniques to perform basic data mining tasks (e.g., clustering, classification, association rules mining) as well as methods for more complex tasks (e.g., data streams and networks mining). A particular emphasis is given to techniques for processing and mining massive amounts of data, that is, data that does not fit in main memory. For most of the techniques presented, a formal computational description is provided in addition to the basic ideas and intuition. Moreover, the students have the opportunity to experiment and apply data mining techniques to a selected problem.

Qualifications
At the end of the course the students should know the most common methods to perform standard data mining tasks, as well as more advanced techniques for more complex tasks and to mine massive amounts of data.

Expected learning outcome
At the end of the course, the student should be able to:

• distinguish and describe the data mining tasks presented in the course
• recognize which method is suitable for a given data mining task
• clearly and precisely describe the algorithms presented in the course
• apply the methods to small toy problems
• undertake experimental evaluation of data mining methods and report the results

Subject overview
Clustering, Classification, Association Rules, Itemsets Mining, Graph Mining, Social Networks, Collaborative Filtering, Finding Similar Items, Data Stream Mining.

Literature

Meddeles ved kursets start.

Website
This course uses e-learn (blackboard).

Prerequisites for participating in the exam
Project. Pass/fail

Assessment and marking:
Oral exam. External examiner, graded after Danish 7 mark scale (10 ECTS). The project includes mandatory milestones to be completed during the course.

Expected working hours
The teaching method is based on three phase model.
Intro phase: 42 hours
Skills training phase: 34 hours, hereof:
 - Tutorials: 14 hours

Educational activities Study phase: 24 hours

Language
This course is taught in English.

Course enrollment
See deadline of enrolment.

Tuition fees for single courses
See fees for single courses.