UnivIS
Information system of Friedrich-Alexander-University Erlangen-Nuremberg © Config eG 
FAU Logo
  Collection/class schedule    module collection Home  |  Legal Matters  |  Contact  |  Help    
search:      semester:   
 
 Layout
 
printable version

 
 
 Also in UnivIS
 
course list

lecture directory

 
 
events calendar

job offers

furniture and equipment offers

 
 

  Knowledge Discovery in Databases (KDD)

Lecturer
Prof. Dr. Klaus Meyer-Wegener

Details
Vorlesung
2 cred.h
nur Fachstudium, Sprache Englisch
Time and place: Mon 16:00 - 17:30, 0.85

Fields of study
WPF INF-MA ab 2
WPF INF-LAG 1-6
WPF INF-LAR 1-6
WF M-BA 4-6
WPF IIS-MA 2-3

Prerequisites / Organisational information
  • Konzeptionelle Modellierung

Inhalt: http://www6.informatik.uni-erlangen.de/DE/teaching/curriculum/kdd/
StudOn: http://www.studon.uni-erlangen.de/crs657784.html

Contents
1. Introduction
2. Know Your Data
3. Data Preprocessing
4. Data Warehousing and On-Line Analytical Processing
5. Data Cube Technology
6. Mining Frequent Patterns, Associations and Correlations: Basic Concepts and Methods
7. Advanced Frequent Pattern Mining
8. Classification: Basic Concepts
9. Classification: Advanced Methods
10. Cluster Analysis: Basic Concepts and Methods
11. Cluster Analysis: Advanced Methods
12. Outlier Detection
13. Trends and Research Frontiers in Data Mining

Recommended literature
  • Han, Jiawei ; Kamber, Micheline ; Pei, Jian: Data Mining: Concepts and Techniques. 3rd ed. Waltham, MA : Morgan Kaufmann, 2012 (The Morgan Kaufmann Series in Data Management Systems). - ISBN 978-0-12-381479-1 (copies are available in the TNZB)
  • Du, Hongbo: Data Mining Techniques and Applications. Andover, UK : Cengage Learning, 2010

  • Witten, Ian H. ; Frank, Eibe ; Hall, Mark A.: Data Mining. Practical Machine Learning Tools and Techniques. 3rd ed. Burlington, MA : Morgan Kaufmann, 2011 (The Morgan Kaufmann Series in Data Management Systems). - ISBN 978-0-12-3748569-0

ECTS information:
Title:
Knowledge Discovery in Databases

Prerequisites
  • Conceptual Modeling

Contents
1. Introduction
2. Know Your Data
3. Data Preprocessing
4. Data Warehousing and On-Line Analytical Processing
5. Data Cube Technology
6. Mining Frequent Patterns, Associations and Correlations: Basic Concepts and Methods
7. Advanced Frequent Pattern Mining
8. Classification: Basic Concepts
9. Classification: Advanced Methods
10. Cluster Analysis: Basic Concepts and Methods
11. Cluster Analysis: Advanced Methods
12. Outlier Detection
13. Trends and Research Frontiers in Data Mining

Literature
  • Han, Jiawei ; Kamber, Micheline ; Pei, Jian: Data Mining: Concepts and Techniques. 3rd ed. Waltham, MA : Morgan Kaufmann, 2012 (The Morgan Kaufmann Series in Data Management Systems). - ISBN 978-0-12-381479-1 (copies are available in the TNZB)
  • Du, Hongbo: Data Mining Techniques and Applications. Andover, UK : Cengage Learning, 2010

  • Witten, Ian H. ; Frank, Eibe ; Hall, Mark A.: Data Mining. Practical Machine Learning Tools and Techniques. 3rd ed. Burlington, MA : Morgan Kaufmann, 2011 (The Morgan Kaufmann Series in Data Management Systems). - ISBN 978-0-12-3748569-0

Additional information
Keywords: Data Mining, KDD
Expected participants: 20

Verwendung in folgenden UnivIS-Modulen
Startsemester SS 2013:
Data Warehousing und Knowledge Discovery in Databases (DWKDD)
Datenbanken in Rechnernetzen und Knowledge Discovery in Databases (DBRNKDD)
Datenstromsysteme und Knowledge Discovery in Databases (DSSKDD)
Knowledge Discovery in Databases und Transaktionssysteme (KDDTAS)

Department: Chair of Computer Science 6 (Data Management)
UnivIS is a product of Config eG, Buckenhof