Title:

Knowledge Discovery in Databases

Code:ZZN
Ac.Year:2006/2007
Term:Winter
Curriculums:
ProgrammeFieldYearDuty
IT-MSC-2MGM.2ndElective
IT-MSC-2MIN.2ndCompulsory
IT-MSC-2MIS.2ndElective
IT-MSC-2MPS-Elective
Language of Instruction:Czech, English
Private info:http://www.fit.vutbr.cz/study/courses/ZZN/private/
Credits:5
Completion:credit+exam (written)
Type of
instruction:
Hour/semLecturesSeminar
Exercises
Laboratory
Exercises
Computer
Exercises
Other
Hours:3900013
 ExamsTestsExercisesLaboratoriesOther
Points:50150035
Guarantor:Zendulka Jaroslav, doc. Ing., CSc. (DIFS)
Lecturer:Zendulka Jaroslav, doc. Ing., CSc. (DIFS)
Instructor:Bartík Vladimír, Ing., Ph.D. (DIFS)
Lukáš Roman, Ing., Ph.D. (DIFS)
Faculty:Faculty of Information Technology BUT
Department:Department of Information Systems FIT BUT
 
Learning objectives:
  To familiarize students with knowledge discovery in data sources, to explain useful knowledge types and the steps of the knowledge discovery process, and to familiarize them with techniques, algorithms and tools used in the process.
Description:
  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 – characterization and discrimination, association rules, classification and prediction, clustering. Complex data type mining. Trends in data mining. Working-out a data mining project by means of an available data mining tool.
Learning outcomes and competences:
  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.
Syllabus of lectures:
 
  1. Introduction - motivation, fundamental concepts, data source and knowledge types.
  2. Data Warehouse and OLAP Technology for knowledge discovery.
  3. Data Preparation.
  4. Mining frequent patterns and associations - basic concepts, efficient and scalable frequent itemset mining methods.
  5. Multi-level association rules, association mining and correlation analysis, constraint-based association rules.
  6. Classification and prediction - basic concepts, decision tree, Bayesian classification, rule-based classification.
  7. Classification by means of neural networks, SVM classifier, other classification methods, prediction.
  8. Cluster analysis - basic concepts, types of data in cluster analysis, partitioning and hierarchical methods.
  9. Other clustering methods.
  10. Mining stream, time-series and sequence data.
  11. Graph mining, social network analysis, multirelational data mining.
  12. Mining object , spatial and multimedia data, text mining, mining the Web.
  13. Applications and Trends in Data Mining.
Syllabus - others, projects and individual work of students:
 
  • Working-out a data mining project by means of an available data mining tool.
Fundamental literature:
 
  • Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Second Edition. Elsevier Inc., 2006, 770 p., ISBN 1-55860-901-3.
  • Dunham, M.H.: Data Mining. Introductory and Advanced Topics. Pearson Education, Inc., 2003, 315 p., ISBN 0-13088-892-3.
Study literature:
 
  • Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Second Edition. Elsevier Inc., 2006, 770 p., ISBN 1-55860-901-3. 
Controlled instruction:
  A mid-term test, formulation of a data mining task, presentation of the project.
Progress assessment:
  A mid-term test, formulation of a data mining task, presentation of the project.
Exam prerequisites:
  Duty credit consists of working-out the project and of obtaining at least 25 points for activities during semester.
 

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