Infopaket

Data Mining Actual Problem

ОК.15

Обов’язкова дисципліна для ОП

Second

2022/2023

2 Semester

4

LPO 1. To have specialized conceptual knowledge that includes modern scientific achievements in the field of computer science and is the basis for original thinking and conducting research, critical reinterpretations of problems in the field of computer science and the boundary scientific fields. LPO 2. To have specialized computer science problem-solving skills/skills necessary for conducting research and/or carrying out innovative activities in order to develop new knowledge and procedures. LPO 8. To develop mathematical models and methods of data analysis (including big data). LPO 9. To develop algorithms and software for data analysis (including big data). LPO 11. To create new algorithms for solving problems in the field of computer science, and evaluate their effectiveness and limitations on their application. LPO 16. To conduct research in the field of computer science.

Know: basic concepts of artificial intelligence and optimization methods; have a notion of contemporary tasks that are solved within the framework of artificial intelligence and data analysis. Be able to: describe the data analysis problem, determine the attributes and types of the problems, and build a data analysis model. Have basic skills: in data analysis, and optimization methods.

The aim of the discipline is to deepen the knowledge of intellectual data analysis and artificial intelligence, and to study the main approaches to solving the main problems - these are the problems of classification, clustering, and finding associative rules. The educational discipline "Actual problems of Data Mining" is a component of the educational-professional program for training specialists at the second (master's) level of higher education in the field of knowledge 12 "Information Technologies" in the specialty 122 "Computer Science", within the educational-professional program - "Informatics". This discipline is a mandatory course in the "Informatics" program. Taught in the 2nd semester of the 1st course of the master's degree in the amount of 120 hours (4 ECTS credits), including lectures - 28 hours, laboratory work - 10 hours, independent work - 80 hours, consultations - 2 hours. The course consists of 2 parts and 2 tests. The discipline is concluded with an exam in the 1st semester.

1. Marchenko O.O., Rossada T.V. Aktualni problemy Data Mining. Navchalno-metodychnyi posibnyk dlya studentiv fakultetu kompyuternyh nauk ta kibernetyky. - Kyiv. - 2017. - 150 p. 2. Kantardzic, M. Data mining: concepts, models, methods, and algorithms. - New York: John Wiley & Sons, 2003. - 343 p. 3. Han, J. Data Mining: Concepts and Techniques / J. Han, M. Kamber. - San Francisco: Morgan Kaufmann Publishers, 2010. - 26 p. 4. Berry, Michael J. A. “DM techniques: for marketing, sales, and customer relationship management “/ Michael J.A. Berry, Gordon Linoff. – 2nd ed. 5. Larose, Daniel T. “Discovering knowledge in data: an introduction to DM” / Daniel T. Larose 6. Leskovec J. Mining of Massive Datasets / Jure Leskovec Anand Rajaraman, Jeffrey David Ullman // Stanford Univ. – 2010. 7. G. Lee, U. Yun A new efficient approach for mining uncertain frequent patterns using minimum data structure without false positives. Future Generation Computational Systems 68:89–110 p., 2017.

Lectures, laboratory classes, individual work.

Semester assessment: 1. Laboratory works: LO 1.1, LO 1.2, LO 2.1, LO 4.1– 25 points / 15 points 2. Course paper: LO 2.1, LO 3.1, LO 4.1 – 25 points / 15 points 3. Current evaluation: LO 2.1, LO 3.1, LO 4.1 – 10 points / 6 points Final assessment: - maximum number of points that can be obtained by the student: 40 points; - learning outcomes that are evaluated: LO 1.1, LO 1.2, LO 2.1 - form of holding: written work.

Ukrainian