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Einrichtungen >> Technische Fakultät (TF) >> Department Informatik (INF) >> Lehrstuhl für Informatik 6 (Datenmanagement) >>
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Knowledge Discovery in Databases (KDD)- Dozentinnen/Dozenten
- Prof. Dr. Richard Lenz, Luciano Melodia, M.A.
- Angaben
- Vorlesung
Online
2 SWS
nur Fachstudium, Sprache Englisch
Zeit: Di 8:15 - 9:45, 00.152-113; Bemerkung zu Zeit und Ort: Aktueller Hinweis: Diese Veranstaltung findet dieses Semester online statt. Weitere Informationen finden Sie im zugehörigen StudOn-Kurs. Information regarding online courses are provided via StudOn.
- Studienfächer / Studienrichtungen
- 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
WF AI-MA 1
- Voraussetzungen / Organisatorisches
- Konzeptionelle Modellierung
- Inhalt
- 1. Introduction
2. Know Your Data
3. Data Preprocessing
4. Mining Frequent Patterns, Associations and Correlations: Basic Concepts and Methods
5. Advanced Frequent Pattern Mining
6. Classification: Basic Concepts
7. Classification: Advanced Methods
8. Cluster Analysis: Basic Concepts and Methods
9. Cluster Analysis: Advanced Methods
10. Outlier Detection
11. Trends and Research Frontiers in Data Mining
- Empfohlene Literatur
- 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-Informationen:
- Title:
- Knowledge Discovery in Databases
- Prerequisites
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- Contents
- 1. Introduction
2. Know Your Data
3. Data Preprocessing
4. Mining Frequent Patterns, Associations and Correlations: Basic Concepts and Methods
5. Advanced Frequent Pattern Mining
6. Classification: Basic Concepts
7. Classification: Advanced Methods
8. Cluster Analysis: Basic Concepts and Methods
9. Cluster Analysis: Advanced Methods
10. Outlier Detection
11. Trends and Research Frontiers in Data MiningThe students will learn about:
the particular challenges of data mining on large sets of data the technologies available for data analysis 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
- Zusätzliche Informationen
- Schlagwörter: Data Mining, KDD
Erwartete Teilnehmerzahl: 20
- Verwendung in folgenden UnivIS-Modulen
- Startsemester SS 2021:
- Data Warehousing und Knowledge Discovery in Databases (DWKDD)
- Datenbanken in Rechnernetzen und Knowledge Discovery in Databases (DBRNKDD)
- Knowledge Discovery in Databases (KDD)
- Knowledge Discovery in Databases and Transaction Systems (KDDTAS)
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