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Course info
FES / DPMDI
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Course description
Department/Unit / Abbreviation
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FES
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DPMDI
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Academic Year
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2023/2024
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Academic Year
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2023/2024
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Title
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Advanced Methods of Data Mining
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Form of course completion
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Examination
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Form of course completion
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Examination
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Accredited / Credits
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Yes,
10
Cred.
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Type of completion
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Combined
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Type of completion
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Combined
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Time requirements
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Lecture
12
[Hours/Semester]
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Course credit prior to examination
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No
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Course credit prior to examination
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No
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Automatic acceptance of credit before examination
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No
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Included in study average
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NO
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Language of instruction
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Czech
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Occ/max
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Automatic acceptance of credit before examination
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No
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Summer semester
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0 / -
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0 / -
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0 / -
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Included in study average
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NO
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Winter semester
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0 / -
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0 / -
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0 / -
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Repeated registration
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NO
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Repeated registration
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NO
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Timetable
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Yes
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Semester taught
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Winter + Summer
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Semester taught
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Winter + Summer
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Minimum (B + C) students
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not determined
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Optional course |
Yes
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Optional course
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Yes
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Language of instruction
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Czech
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Internship duration
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0
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No. of hours of on-premise lessons |
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Evaluation scale |
S|N |
Periodicity |
každý rok
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Periodicita upřesnění |
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Fundamental theoretical course |
No
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Fundamental course |
No
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Fundamental theoretical course |
No
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Evaluation scale |
S|N |
Substituted course
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None
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Preclusive courses
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N/A
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Prerequisite courses
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N/A
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Informally recommended courses
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N/A
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Courses depending on this Course
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N/A
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Histogram of students' grades over the years:
Graphic PNG
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XLS
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Course objectives:
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The course is aimed at clarification of theoretical principals and solution of practical problems with application of Data Mining, Web Mining and Text Mining. Attention is paid to tasks of data transformation, pre-processing and verification, furthermore selection of the appropriate methods, process evaluation and results' interpretation.
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Requirements on student
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Students have to take part in seminars.
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Content
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Předmět je zaměřen na objasnění teoretických principů i řešení praktických problémů s využitím metod Data Miningu, Web Miningu a Text Miningu. Pozornost je věnována otázkám transformace, předzpracování a verifikace dat, zvolení vhodných metod, vyhodnocení procesu a interpretace výsledků. Velký důraz je kladen na řešení samostatné úlohy na reálných datech pod dohledem vyučujícího.
V rámci předmětu se vychází z teoretických základů jednotlivých metod a algoritmů z oblasti Data Miningu a Knowledge Discovery s cílem rozvinout možnosti jejich využití, ohodnotit výsledky získané použitím data miningu; posoudit vhodnost a použitelnost inteligentních přístupů pro řešení reálných problémů; formulovat a řešit úlohy dobývání znalostí z databází pro reálná data, která mohou pocházet jak z homogenních, tak heterogenních datových struktur; aplikovat metody reprezentace a zpracování znalostí při vývoji softwarových systémů apod.
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Activities
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Fields of study
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Guarantors and lecturers
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Literature
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Recommended:
Tufféry, Stéphane. Data mining and statistics for decision making. Chichester: John Wiley & Sons, 2011. ISBN 978-0-470-68829-8.
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Recommended:
WITTEN, I.H., FRANK, E., HALL, M.A. Data Mining: Practical Machine Learning Tools and Techniques. Amsterdam, 2011.
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Recommended:
Aggarwal, Charu C. Mining text data. New York: Springer Science+Business Media, 2012. ISBN 978-1-4614-3222-7.
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Recommended:
MINER, G. Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications. Amsterdam, 2012.
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Recommended:
Feldman. The text mining handbook : advanced approaches in analyzing unstructured data. New York, 2007.
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Prerequisites - other information about course preconditions |
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Competences acquired |
Student will be able to suitably apply methods of spatial analyses and visualisation methods during solving spatially-oriented tasks. |
Teaching methods |
- Monologic (reading, lecture, briefing)
- Dialogic (discussion, interview, brainstorming)
- Work with text (with textbook, with book)
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Assessment methods |
- Home assignment evaluation
- Student performance assessment
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