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Data Mining
Major: Publishing and printing computer technologies and systems
Code of Subject: 7.186.01.E.17
Credits: 3
Department: Publishing Information Technologies
Lecturer: Professor Yuriy Rashkevych, PhD, Dr.Sc.
Semester: 2 семестр
Mode of Study: денна
Learning outcomes:
By the end of this course, the student should be able to:
• Know the basic concepts and stages of data mining, as well as the structure and functioning of integrated information and analytical systems.
• Know the mathematical formulation of the main tasks of data analysis and methods of their solution.
• Know the basic algorithms of data mining.
• Be able to use the basic methods of data mining to solve practical problems.
• Ability to apply knowledge in practice, work autonomously and in a group.
• Know the basic concepts and stages of data mining, as well as the structure and functioning of integrated information and analytical systems.
• Know the mathematical formulation of the main tasks of data analysis and methods of their solution.
• Know the basic algorithms of data mining.
• Be able to use the basic methods of data mining to solve practical problems.
• Ability to apply knowledge in practice, work autonomously and in a group.
Required prior and related subjects:
not any
Summary of the subject:
Models and methods of data mining, classification and regression, methods of constructing classification rules, methods of constructing tree solutions, constructing mathematical functions, forecasting time series, searching for associative rules, sequential analysis, clustering algorithms, adaptive clusterization methods, formal qualitative criteria.
Recommended Books:
1. Р.О.Ткаченко,Н.О. Кустра, О.М.Павлюк, У.В.Поліщук Засоби штучного інтелекту. Навч.посібник. Львів,2014.
2. А.А.Барсегян и др.. Технологии анализа данных. – Санкт-Петербург: БХВ Петербург, 2007. – 376 с.
3. Data Mining Concepts and Techniques. Jiawei Han, Micheline Kamber, Jian Pei Simon Fraser University. - Morgan Kaufmann Publishers, 2012. – 693 pp.
2. А.А.Барсегян и др.. Технологии анализа данных. – Санкт-Петербург: БХВ Петербург, 2007. – 376 с.
3. Data Mining Concepts and Techniques. Jiawei Han, Micheline Kamber, Jian Pei Simon Fraser University. - Morgan Kaufmann Publishers, 2012. – 693 pp.
Assessment methods and criteria:
• Written labs reports, intermediate test, oral presentation (30%);
• Final examination (70%).
• Final examination (70%).