Data Mining and network analysis IDN0110
IDN0110 / ITI8730: Data Mining and network analysis Taught by: Sven Nõmm EAP: 6.0
Examination (final project defense) and Consultations times
4.01 16:00-17:00 Consultation, make up for Closed book test 2. (If you wish to defend your project this date please contact lecturer in advance.)
10.01 16:00- 17:45 Examination (final project defense)
17.01 16:00 – 17:00 Consultation
23.01 16:00- 17:45 Examination (final project defense)
Lectures: Wednesdays 14:00-15:30 ICT-A1 Labs: Thursdays 16:00-17:30 ICT-401
Consultation: by appointment only Please do not hesitate to ask for appointment!!! For communication please use the following e-mail: firstname.lastname@example.org
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:
- 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,
- Spatial Data, Graph Data, Web Data, Social Network Analysis
- 2x mandatory closed book tests. Each test gives 10% of the final grade.
- 3x mandatory home assignments (Computational assignment +short write up.) Each assignment gives 10% of the final grade.
- final exam (gives 50 % of the final grade): Written report on assigned topic + discussion with lecturer.
Exam prerequisites: both 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 ained.ttu.ee environment. Course enrollment process will be conducted during the first lecture.
Lecture 1 Introduction and data preparation
Lecture 2 Similarity and distance
Lecture 3 Cluster analysis
Lecture 4 Classification
Lecture 5 Anomaly and Outlier Analysis
Practice 5 Implementation of EM - Algorithm
Lecture 6 Association Pattern Mining
Lecture 7 Similarity and Distance 2
Lecture 8 Text Data Mining
Lecture 9 Time Series Mining
Lecture 10 Data Streams Mining
Lecture 11 Graph Data Mining
Lecture 12 Social Network Analysis
Lecture 13 Privacy preserving data mining