An introduction to general topics of data mining and its applications.

After completion of the course, the student have adopted the elementary concepts and techniques of Data Mining and trained for their practical applications.

An introduction to Data Mining. Basic terms and definitions. Overview of Data Mining techniques, goals and problems. Data: types, preprocessing, quality, measures of similarity and dissimilarity. Data preparation: sumarization, cleansing, transformation, integration, reduction and discretization. Association rules, correlation and analysis of frequent patterns. Classification techniques: basic concepts and metrics. Statistical based, distance based, and tree based algorithms. Rule based algorithms, neural networks, support vector machines. Cluster analysis - basic concepts and algorithms. Hierarchical and partitional algorithms. Outlier Analysis. Data and results visualization.

Student will master specific data mining techniques through practical examples with different data collections using SPSS Modeler and Python tools.

None.

Total assigned hours: 90

New material: 20
Elaboration and examples (recapitulation): 10

Auditory exercises: 45
Laboratory exercises: 0
Calculation tasks: 0
Seminar paper: 0
Project: 0
Consultations: 0
Discussion/workshop: 0
Research study work: 0

Review and grading of calculation tasks: 0
Review and grading of lab reports: 0
Review and grading of seminar papers: 0
Review and grading of the project: 0
Test: 0
Test: 10
Final exam: 5

Activity during lectures: 0
Test/test: 30
Laboratory practice: 0
Calculation tasks: 0
Seminar paper: 0
Project: 0
Final exam: 70
Requirement for taking the exam (required number of points): 0

Pang-Ning Tan, Michael Steinbach, Anuj Karpatne, Vipin Kumar: Introduction to Data Mining, 2nd ed, Pearson Education, 2019.; Xindong Wu, Vipin Kumar (eds.): The Top Ten Algorithms in Data Mining, CRC Press, 2009.; Charu C. Aggarwal: Data Mining The Textbook, Springer, 2015.