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Lecture "Discrete Topics in Data Mining" WS 2012/13, 3 ECTS credits


Dr Pauli Miettinen



Two hours per week, on Tuesdays, from noon till 2 pm. Place: room 007 in building E2 1 (bioinformatics).

Schedule & Slides

The schedule of the course and the slides are here.

The articles related to each topic are listed below. To access the PDFs, you need the username and password.

Essay Topics

The essay topics are here.

Guidelines to Return the Essays

The essays must be returned in PDF format via e-mail to the lecturer (see slides or lecturer's home page for the address). The deadline for the essays, unless otherwise specified, is two weeks from the date the topics were given at 14:00 hours (2 pm). Failure to submit the essay on time will give you a failed grade. The time of the submission is the timestamp of the mail as shown by the lecturer's e-mail system. It is advisable to send the essays before noon, so that if you have not received a response, you can ask the status of your essay before the lecture.

Every essay you return must have the following information:

In addition, it is advisable to start the subject line of the mail with "DTDM" and have the word "essay" somewhere in the subject. This helps me to notice the purpose of the mail and (hopefully) prevents the spam filters from filtering the mails.

There are no page limits for the essays, but I expect a good essay to take between two to five A4-pages in 10pt font and 2.5cm margins all around (you are free to use other font sizes and margins as long as the text stays legible).

The essays must follow the normal scientific citation practices. Substantial failure to do so will cause a failure of the essay. The essays may contain (numbered) section and subsection headings if the author so prefers.


The course will provide an overview of some important topics in data mining. The purpose of the course is to concentrate on the ideas and intuition behind these topics, with the aim that after the course, the students can follow the current research on the topics.

The exact topics covered on this lecture will be announced later (and students' preferences can be considered), but tentatively we will cover at least pattern set mining, graph mining, and significance testing (in pattern set mining).

The course will have two hours of lectures every week. There will not be any homework sessions. Instead, students have to write longer essays/reports on the topics covered on the lectures.


Students are expected to have passed either Information Retrieval & Data Mining or Machine Learning core lectures, or hold equivalent knowledge.

Requirements for Passing the Course

Grading System

The essays are graded in failed/passed/excellent grades. Out of the five essays, you need to have a passing grade from at least four to be allowed to take the final exam. If you are allowed to take and pass the final exam, then each excellent grade from essays will improve your final grade by 1/3 of what you got from the final exam. That is, if you got 2.0 from the final exam and you have one excellent grade, your final grade will be 1.7; if you have three or more excellent grades, it will be 1.0.

Articles for the Topics

Topic I, Pattern Set Mining

Topic II, Graph Mining

Topic III, Significance Testing

Topic IV, Tensors

Background Literature