Course detail
Knowledge Discovery in Databases
FIT-ZZNAcad. year: 2019/2020
Basic concepts concerning knowledge discovery in data, relation of knowledge discovery and data mining. Data sources for knowledge discovery. Principles and techniques of data preprocessing for mining. Systems for knowledge discovery in data, data mining query languages. Data mining techniques association rules, classification and prediction, clustering. Mining unconventional data - data streams, time series and sequences, graphs, spatial and spatio-temporal data, multimedia. Text and web mining. Working-out a data mining project by means of an available data mining tool.
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Department
Learning outcomes of the course unit
- Students get a broad, yet in-depth overview of the field of data mining and knowledge discovery.
- They are able both to use and to develop knowledge discovery tools.
- Student learns terminology in Czech and English.
- Student gains experience in solving projects in a small team.
- Student improves his ability to present and defend the results of projects.
Prerequisites
- Basic knowledge of probability and statistics.
- Knowledge of database technology at a bachelor subject level.
Co-requisites
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
Exam prerequisites:
Duty credit consists of working-out the project, defending project results and of obtaining at least 24 points for activities during semester.
Course curriculum
Work placements
Aims
Specification of controlled education, way of implementation and compensation for absences
Recommended optional programme components
Prerequisites and corequisites
Basic literature
Recommended reading
Classification of course in study plans
- Programme IT-MSC-2 Master's
branch MMI , 0 year of study, winter semester, elective
branch MBI , 2 year of study, winter semester, compulsory
branch MSK , 2 year of study, winter semester, compulsory-optional
branch MMM , 0 year of study, winter semester, elective
branch MBS , 0 year of study, winter semester, compulsory-optional
branch MPV , 0 year of study, winter semester, compulsory-optional
branch MIS , 2 year of study, winter semester, compulsory-optional
branch MIN , 2 year of study, winter semester, compulsory
branch MGM , 2 year of study, winter semester, elective - Programme MITAI Master's
specialization NBIO , 0 year of study, winter semester, compulsory
specialization NISD , 2 year of study, winter semester, compulsory
specialization NSEN , 0 year of study, winter semester, elective
specialization NVIZ , 0 year of study, winter semester, elective
specialization NGRI , 0 year of study, winter semester, elective
specialization NSEC , 0 year of study, winter semester, elective
specialization NCPS , 0 year of study, winter semester, elective
specialization NHPC , 0 year of study, winter semester, elective
specialization NNET , 0 year of study, winter semester, elective
specialization NMAL , 0 year of study, winter semester, elective
specialization NVER , 0 year of study, winter semester, elective
specialization NIDE , 0 year of study, winter semester, elective
specialization NEMB , 0 year of study, winter semester, elective
specialization NSPE , 0 year of study, winter semester, elective
specialization NADE , 0 year of study, winter semester, elective
specialization NMAT , 0 year of study, winter semester, elective
specialization NISY , 0 year of study, winter semester, compulsory
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
- Introduction - motivation, fundamental concepts, data source and knowledge types.
- Data Preparation - characteristics of data.
- Data Preparation - methods.
- Data Warehouse and OLAP Technology for knowledge discovery.
- Mining frequent patterns and associations - basic concepts, efficient and scalable frequent itemset mining methods.
- Multi-level association rules, association mining and correlation analysis, constraint-based association rules.
- Classification and prediction - basic concepts, decision tree, Bayesian classification, rule-based classification.
- Classification by means of neural networks, SVM classifier, other classification methods, prediction.
- Cluster analysis - basic concepts, types of data in cluster analysis, partitioning and hierarchical methods.
- Other clustering methods. Mining in biological data.
- Introduction to mining data stream, time-series and sequence data.
- Introduction to mining in graphs, spatio-temporal data, moving object data and multimédia data.
- Text mining, mining the Web.
Project
Teacher / Lecturer
Syllabus
- Working-out a data mining project by means of an available data mining tool.