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  Knowledge Discovery in Databases (KDD)

Lecturer
Prof. Dr. Klaus Meyer-Wegener

Details
Vorlesung
2 cred.h
nur Fachstudium, Sprache Englisch
Time and place: Tue 16:00 - 17:30, K2-119

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
WF MT-MA-BDV ab 1

Prerequisites / Organisational information
  • Konzeptionelle Modellierung

StudOn: http://www.studon.uni-erlangen.de/crs1164253.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

The students will learn about:

  • the particular challenges of data mining on large sets of data

  • the technologies available for data analysis

  • systems offering these technologies

  • the process of data mining

  • applications

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 2015:
Data Warehousing und Knowledge Discovery in Databases (DWKDD)
Datenstromsysteme und Knowledge Discovery in Databases (DSSKDD)

Department: Chair of Computer Science 6 (Data Management)
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