Title:

Knowledge Discovery in Databases

Code:ZZN
Ac.Year:2017/2018
Term:Winter
Curriculums:
ProgrammeBranchYearDuty
IT-MSC-2MBI2ndCompulsory
IT-MSC-2MBS-Compulsory-Elective - group S
IT-MSC-2MGM2ndElective
IT-MSC-2MIN2ndCompulsory
IT-MSC-2MIS2ndCompulsory-Elective - group N
IT-MSC-2MMI-Elective
IT-MSC-2MMM-Elective
IT-MSC-2MPV-Compulsory-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
Burgetová Ivana, Ing., Ph.D., DIFS
Zendulka Jaroslav, doc. Ing., CSc., DIFS
Instructor:Bartík Vladimír, Ing., Ph.D., DIFS
Faculty:Faculty of Information Technology BUT
Department:Department of Information Systems FIT BUT
Schedule:
DayLessonWeekRoomStartEndLect.Gr.St.G.EndG.
TuelecturelecturesD020611:0013:501MITxxxx
TuelecturelecturesD020611:0013:502MIT10 MBI10 MBI
TuelecturelecturesD020611:0013:502MIT13 MIN13 MIN
Wedexam - řádná2018-01-10D10512:0014:501MIT
Wedexam - řádná2018-01-10D10512:0014:502MIT
Wedprohlídka opravených písemek2017-11-15C22813:0013:502MIT
Friexam - 2. oprava2018-02-02G20208:0010:501MIT
Friexam - 2. oprava2018-02-02G20208:0010:502MIT
Friexam - 1. oprava2018-01-26G20208:0010:501MIT
Friexam - 1. oprava2018-01-26G20208:0010:502MIT
 
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  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.
Knowledge and skills required for the course:
  
  • Basic knowledge of probability and statistics.
  • Knowledge of database technology at a bachelor subject level. 
Subject specific 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.
Generic learning outcomes and competences:
  
  • Student learns terminology in Czech ane English language.
  • Student gains experience in solving projects in a small team.
  • Student improves his ability to present and defend the results of projects.
Syllabus of lectures:
 
  1. Introduction - motivation, fundamental concepts, data source and knowledge types.
  2. Data Preparation - characteristics of data.
  3. Data Preparation - methods.
  4. Data Warehouse and OLAP Technology for knowledge discovery.
  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.
  10. Other clustering methods. Mining in biological data.
  11. Introduction to mining data stream, time-series and sequence data.
  12. Introduction to mining in graphs, spatio-temporal data, moving object data and multimédia data. 
  13. Text mining, mining the Web.
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. Third Edition. Morgan Kaufmann Publishers, 2012, 703 p., ISBN 978-0-12-381479-1.
  • Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Second Edition. Elsevier Inc., 2006, 770 p., ISBN 1-55860-901-3.  
Study literature:
 
  • Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Third Edition. Morgan Kaufmann Publishers, 2012, 703 p., ISBN 978-0-12-381479-1.
  • Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Second Edition. Elsevier Inc., 2006, 770 p., ISBN 1-55860-901-3.
  • Bishop, C.M: Pattern Recognition and Machine Learning. Springer, 2006, 738 p. ISBN 0387310738.     
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, defending project results and of obtaining at least 24 points for activities during semester.