Infopaket

Data Mining Actual Problem

ОК.13

Обов’язкова дисципліна для ОП

Second

2023/2024

2 Semester

4

PLO3. To master new data tools by processing weblogs, text mining and machine learning, for forecasting business processes and situational management, sentimental analysis of reviews, development of advisory systems for the field of electronic commerce, media, social networks, banking, advertising, etc.

Distance form

To know basic concepts of artificial intelligence and optimization methods; have a modern understanding of the main problems that are solved with the methods of artificial intelligence and data analysis. To be able to describe the task of data analysis, determine the attributes and type of problem, build a model.

Discipline aim. The purpose of the discipline is to broaden knowledge of data mining and artificial intelligence, studying the basic approaches to solving basic data analysis problems. These are the tasks of classification, clustering, search for associative rules. The learning course is devoted to the main problems of data mining: classification, clustering, search for associative rules. The main classes of algorithms for solving the corresponding problems are considered. A comparative analysis of approaches and possible modifications are given. In particular, algorithms for constructing decision trees, adaptive clustering are studied. The main considerations associated with the use of artificial neural networks for data analysis problems are investigated.

1. Larose, Daniel T. “Discovering knowledge in data: an introduction to DM” / Daniel T. Larose 2. Leskovec J. Mining of Massive Datasets / Jure Leskovec Anand Rajaraman, Jeffrey David Ullman // Stanford Univ. – 2010. 3. G. Lee,U. Yun A new efficient approach for mining uncertain frequent patterns using minimum data structure without false positives. Future Generation Computational Systems 68:89–110p., 2017. 4. M.K. Gupta, P. Chandra A comparative study of clustering algorithms. In: Proceedings of the 13th INDIACom-2019; IEEE Conference ID: 461816; 6th International Conference on “Computing for Sustainable Global Development”, 2019.

Lectures, laboratory classes, individual work.

Semester assessment: 1. Test: LO 1.1, LO 1.2, LO 2.1 – 15 points / 9 points 2. Laboratory works: LO 2.1, LO 4.1– 15 points / 9 points 3. Course paper: LO 3.1, LO 4.1 – 20 points / 12 points 4. 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, English