157-0386/01 – Data mining (DM)
| Gurantor department | Department of Systems Engineering | Credits | 5 |
| Subject guarantor | doc. Dr. Ing. Miroslav Hudec | Subject version guarantor | doc. Dr. Ing. Miroslav Hudec |
| Study level | undergraduate or graduate | Requirement | Compulsory |
| Year | 1 | Semester | summer |
| | Study language | Czech |
| Year of introduction | 2020/2021 | Year of cancellation | |
| Intended for the faculties | EKF | Intended for study types | Follow-up Master |
Subject aims expressed by acquired skills and competences
The aim of the course is to understand and master the main approaches and methods for mining and interpreting information and knowledge from the data. The lectures provide a theoretical basis for understanding data mining. The seminars provide space for demonstrating tasks, examining various cases and discussion.
Teaching methods
Lectures
Individual consultations
Tutorials
Summary
The aim of the course is to understand and master the main approaches and methods for mining and interpreting information and knowledge from the data. The lectures provide a theoretical basis for understanding data mining. The seminars provide space for demonstrating tasks, examining various cases and discussion.
Compulsory literature:
Recommended literature:
Way of continuous check of knowledge in the course of semester
Credit:
- active participation in seminars, finished project and test
- getting at least 20 points of 40 during semester.
Exam:
- questions from the given topics
- getting at least 31 points of 60 at the exam.
E-learning
Students have all relevant presentations from lectures and instructions in LMS Moodle
Other requirements
Active participation at the seminars (at least 60 %).
Project according to the expected structure and content.
Obtained more than 50% of the total points.
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
1. Introduction into data mining (definition of data mining, relation to the other scientific disciplines, clarification of the basic concepts).
2. Data types (numeric, categorical, text, fuzzy data). Logical, statistical and algebraic view of data. Categorization of data mining requirements.
3. Steps of data mining: data pre-processing, data cleaning, mining and interpretation of results.
4. Methods and properties of direct and indirect data mining. Task categorization of tasks and classification of methods.
5. Classical and flexible classification, classical and flexible aggregation.
6. Association rules, decision trees and network analysis.
7. Statistical and logical data summaries.
8. Computational intelligence in data mining.
9. Aggregation and evaluation of opinions.
10. Basic procedures of text mining, text categorization, classification of text documents.
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction