Knowledge Discovery in Databases

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

The lecture is based on:

• 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)

Also recommended:

• 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

Target Audience

• Students of Computer Science (Master) in the second semester
• Students of Medical Technology (Master), specialization in "Medical Image and Data Processing"
• Students of International Information Systems (Master) in the second semester

Additional Information

• Lecture offered: summer semester (SS)
• Language: English
• Weekly hours in semester (SWS): 2+0
• Credit points: 2,5
• Exam: see module
• Contact person: Prof. Dr. Klaus Meyer-Wegener