Data Mining (ITI8730)

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Information for perspective students:

Lecture schedule and slides content are tentative. Please follow the course page in TalTech Moodle for up to date information and lecture content!!!

The course is open to students with valid TalTech UniID! The course targets M.Sc. curricula students. It is expected that the students are familiar with the Calculus, Linear algebra, Probability, Statistics and possess basic to intermediate knowledge of at least one programming language. This course is not recommended for students of B.Sc. curricula.

Code to join course page in Moodle and MS Teams will be provided to the students via ÕIS e-mail on Monday September the 4th.

Those planning to use their own computers please install "R" and "R-studio".

Fall 2023

ITI8730: Data Mining and network analysis

Old code for this course is IDN0110

Taught by: Sven Nõmm

Teaching assistants Ilja Matjas, Rajesh Kalakoti

EAP: 6.0

Lectures: Tuesdays 12:15 - 13:45 ICT-A1

Labs (practices): Thursdays 14:00 - 15:30 ICT-404

Link to join MS Teams

Consultation: by appointment only Please do not hesitate to ask for appointment!!! For communication please use the following e-mail:

Prerequisites to join the course

Students are expected to be familiar with the foundations of Calculus, Linear algebra, Probability theory and Statistics and possess the knowledge of at least one programming language.


The course aims to provide knowledge of theory behind different methods of data mining and develop practical skills in applying those methods on practice. Is is spanned around four "super problems" of data mining:

  • Clustering
  • Classification
  • Association pattern mining
  • Outlier analysis

Main topics of the course:

  • Data types and Data Preparation
  • Similarity and Distances, Association Pattern Mining,
  • Cluster Analysis, Classification, Outlier analysis
  • Data streams, Text Data, Time Series, Discrete Sequences,
  • Graph Data, Social Network Analysis


  • 2x mandatory closed book tests. Each test gives 10% of the final grade. One make-up attempt for each test.
  • 3x mandatory home assignments (Computational assignment +short write up.) Each assignment gives 10% of the final grade. Late (after deadline) assignments are accepted with penalty of 10% for each day except Saturdays and Sundays.
  • final exam (gives 50 % of the final grade): Written report on assigned topic + discussion with lecturer.

Exam prerequisites: All 2 closed book tests are accepted (graded as 51 or higher), all 3 home assignments are accepted (graded as 51 or higher).

Home assignments, code examples, data files and useful links will be distributed by means of Moodle environment. Course enrollment process in Moodle TBA.

Lectures and Time line

05.09.23 Distance function


12.09.23 Cluster analysis I


19.09.23 Cluster analysis II


Slides (Practice)

26.09.23 Anomaly and outlier analysis


03.10.23 Classification I


10.10.23 Classification II


17.10.23 Regression analysis


24.10.23 Association Pattern mining


31.10.23 Closed Book Test I

07.11.23 Distance and Similarity II


14.11.23 Mining the Time series


21.11.23 Mining data streams


28.11.23 Text data mining


05.12.23 Graph data mining and Social analysis



12.12.23 Privacy preserving data mining


19.12.23 Closed Book Test II