MTAT.03.249 Combinatorial Data Mining Algorithms
MTAT.03.249 Kombinatoorsed andmekaeve algoritmid

  • Seminars: Wednesdays 14:15 Liivi 2-122
  • Office hours: Mondays Liivi 2-326. Please arrange exact details of the meeting by email.
  • Questions: swen@math.ut.ee
  • The first seminar starts on the 16th of September
  • There are no course books. Presentations cover articles.
  • The seminar ends with a project organised together with Data Mining course
  • Lectures and seminar sessions are held in English or in Estonian depending on circumstances.
  • Final reports should be written preferably in English.

What and Why

The course aims to give a comprehensive overview of frequent itemset mining and its generalisations to more structured data. The main motivation is the conceptual simplicity for a computer science student---most algorithms do not require extensive knowledge in statistics. Instead, various authors present elegant algorithmic tricks and clever use of data structures. As such it should be more accessible to wider audience. Besides its simplicity, the frequent patter mining has be proven to be a useful as a descriptive tool. It has been widely used in many practical applications starting form the standard market basket analysis and ending with analysis of complex data.

Requirements

There are no formal prerequisites to the seminar. However, basic knowledge in programming and basic math is advisable. Also, one needs reasonable English skills to complete the course report. If the formal requirements of the ÕIS do not permit registration then write me an email or talk with me. After that we decide whether to enroll you or not.

To pass the course

  • You have to give at least one presentation about the topics covered in this course.
  • You must actively participate in most seminars or otherwise you do not pass the seminar. Namely, student gets grade F if he or she misses 3 or more seminars. In reasonable circumstances, it is possible to compensate missed seminars by extra work. Details are determined by individual agreements with the lecturer.
  • You must write a survey or give an extra presentation or do a project work. Namely, if a student participates in Data Mining course and does a corresponding project work in a topic that is connected with the course, then the student can skip the survey provided that he or she presents the project to me. I will not grade the project in this course, but the project has to be positively graded in Data Mining course.
  • The requirements for the surveys are standard: they must cover the topic and be understandable for the fellow students. The survey should concentrate on the core contribution and if necessary refer to other sources to introduce advanced results or further developments.
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