Title:

Knowledge Discovery in Databases

Code:ZZN
Ac.Year:2009/2010
Term:Winter
Curriculums:
ProgrammeBranchYearDuty
IT-MSC-2MBI2ndCompulsory
IT-MSC-2MBS-Compulsory-Elective - group S
IT-MSC-2MGM2ndElective
IT-MSC-2MGM.2ndElective
IT-MSC-2MIN2ndCompulsory
IT-MSC-2MIN.2ndCompulsory
IT-MSC-2MIS2ndCompulsory-Elective - group N
IT-MSC-2MIS.2ndElective
IT-MSC-2MMI-Elective
IT-MSC-2MMM-Elective
IT-MSC-2MPS-Elective
IT-MSC-2MPV1stCompulsory-Elective - group D
IT-MSC-2MSK2ndCompulsory-Elective - group M
Language:Czech
Private info:http://www.fit.vutbr.cz/study/courses/ZZN/private/
Credits:5
Completion:accreditation+exam (written)
Type of
instruction:
Hour/semLecturesSem. ExercisesLab. exercisesComp. exercisesOther
Hours:3900013
 ExaminationTestsExercisesLaboratoriesOther
Points:51150034
Guarantee:Zendulka Jaroslav, doc. Ing., CSc., DIFS
Lecturer:Bartík Vladimír, Ing., Ph.D., DIFS
Rudolfová Ivana, Ing., Ph.D., DIFS
Zendulka Jaroslav, doc. Ing., CSc., DIFS
Instructor:Bartík Vladimír, Ing., Ph.D., DIFS
Jaša Petr, Ing., DIFS
Krajíček Jiří, Ing., 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 - methods.
  4. Data Preparation - characteristics of data.
  5. Mining frequent patterns and associations - basic concepts, efficient and scalable frequent itemset mining methods.
  6. Multi-level association rules, association mining and correlation analysis, constraint-based association rules.
  7. Classification and prediction - basic concepts, decision tree, Bayesian classification, rule-based classification.
  8. Classification by means of neural networks, SVM classifier, other classification methods, prediction.
  9. Cluster analysis - basic concepts, types of data in cluster analysis, partitioning and hierarchical methods. Other clustering methods.
  10. Introduction to mining data stream, time-series and sequence data, mining in graphs.
  11. Introduction to multirelational data mining, spatial and multimedia data. Privacy protection in data mining.
  12. Mining in biological data.
  13. Text mining, mining the Web.
Syllabus - others, projects and individual work of students:
  1. Working-out a data mining project by means of an available data mining tool.
Fundamental literature:
  1. Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Second Edition. Elsevier Inc., 2006, 770 p., ISBN 1-55860-901-3.
  2. Dunham, M.H.: Data Mining. Introductory and Advanced Topics. Pearson Education, Inc., 2003, 315 p., ISBN 0-13088-892-3.
Study literature:
  1. 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. The minimal number of points which can be obtained from the final exam is 20. Otherwise, no points will be assigned to the student.
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.