157-0386/01 – Data mining (DM)

Gurantor departmentDepartment of Systems Engineering and InformaticsCredits5
Subject guarantordoc. Dr. Ing. Miroslav HudecSubject version guarantordoc. Dr. Ing. Miroslav Hudec
Study levelundergraduate or graduateRequirementCompulsory
Year1Semesterwinter
Study languageCzech
Year of introduction2020/2021Year of cancellation
Intended for the facultiesEKFIntended for study typesFollow-up Master
Instruction secured by
LoginNameTuitorTeacher giving lectures
HUD0118 doc. Dr. Ing. Miroslav Hudec
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Credit and Examination 2+2

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:

BERKA, Petr. Dobývání znalostí z databází. Praha: Academia, 2003. ISBN 80-200-1062-9. HUDEC, Miroslav. Fuzzy logika pre hospodársku informatiku. Bratislava: Ekonóm, 2015. ISBN 978-80-225-4100-8. SKALSKÁ, Hana. Data mining a klasifikační modely. Hradec Králové: Gaudeamus, 2010. ISBN: 978-80-7435-088-7.

Recommended literature:

AGGRAWAL, Charu. Data Mining: The Textbook. Cham: Springer, 2015. ISBN 978-3-319-14141-1. BRAMER, Max. Principles of data mining. London: Springer-Verlag, 2013. ISBN 978-1-4471-4884-5. HUDEC, Miroslav. Fuzziness in Information Systems - How to Deal with Crisp and Fuzzy Data in Selection, Classification, and Summarization. Cham: Springer, 2016. ISBN 978-3-319-42516-0.

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

Full-time form (validity from: 2020/2021 Summer semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of pointsMax. počet pokusů
Credit and Examination Credit and Examination 100 (100) 51
        Credit Credit 40 (40) 20
                Written test Written test 15  5
                project Project 25  10
        Examination Examination 60  31 3
Mandatory attendence participation: Active participation in seminars (at least 60 %) Project according to the expected structure and content. Getting at least 20 points of 40 during semester. Getting at least 31 points of 60 at the exam.

Show history

Conditions for subject completion and attendance at the exercises within ISP: Active participation in seminars (at least 60 %) Project according to the expected structure and content. Getting at least 20 points of 40 during semester. Getting at least 31 points of 60 at the exam.

Show history

Occurrence in study plans

Academic yearProgrammeBranch/spec.Spec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2024/2025 (N0688A050001) Information and Knowledge Management DZ P Czech Ostrava 1 Compulsory study plan
2024/2025 (N0311A050040) Economics P Czech Ostrava 2 Choice-compulsory type B study plan
2023/2024 (N0688A050001) Information and Knowledge Management DZ P Czech Ostrava 1 Compulsory study plan
2023/2024 (N0311A050040) Economics P Czech Ostrava 2 Choice-compulsory type B study plan
2022/2023 (N0688A050001) Information and Knowledge Management DZ P Czech Ostrava 1 Compulsory study plan
2021/2022 (N0688A050001) Information and Knowledge Management DZ P Czech Ostrava 1 Compulsory study plan
2020/2021 (N0688A050001) Information and Knowledge Management DZ P Czech Ostrava 1 Compulsory study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner

Assessment of instruction



2023/2024 Winter
2021/2022 Summer