Modulcode:	infKDDM-01a
Englische Bezeichnung:	Knowledge Discovery and Data Mining
Modulverantwortliche(r):	Prof. Dr. Matthias Renz
Turnus:	unregelmäßig (WS18/19 WS19/20 WS20/21 WS21/22 WS22/23 WS23/24 WS24/25)
Präsenzzeiten:	4V 2Ü
ECTS:	8
Workload:	60 h lectures, 30 h exercises, 150 h self studies
Dauer:	ein Semester
Modulkategorien:	BSc-Inf-WP (BSc Inf (21)) WI (BSc Inf (15)) MSc-Inf-WP (MSc Inf (21)) 2F-MEd-Inf-WP (MEd-Hdl Inf (21)) 2F-MA-Inf-WP (2F-MA Inf (21)) MSc-WInf-WP-Inf (MSc WInf (21)) WI (MSc Inf (15)) WI (MSc WInf (15))
Lehrsprache:	Englisch
Voraussetzungen:	Inf-ADS

Kurzfassung:

Knowledge Discovery is the nontrivial process of discovering interesting, valid, novel, and potentially useful information in huge collections. It is a multidisciplinary field drawing work from areas including data management and databases, statistics, pattern recognition, machine learning, information retrieval, recommendation systems, knowledge-based systems, high-performance computing, and data visualization among others. Data Mining is the key-component of the knowledge discovery process that performs the analysis of the data to reveal new information. Though data mining already emerged during the late 1980s, in the context of data-intensive scientific discovery (founding the new age of science), it became a very important field for many applications in academia and industry. This lecture will introduce the the field knowledge discover by focusing on basic concepts and algorithms for data mining and related concepts for data pre-processing.

Lernziele:

In this lecture, students will become an understanding of the field Knowledge Discovering and learn the principal and most important techniques, methods and tools associated with data mining, including frequent itemset mining and association rule mining, classification, clustering, and outlier detection.

Lehrinhalte:

• Introduction to Data Mining and Knowledge Discovery in Databases
• Data preprocessing, feature selection, similarity and distance functions
• Frequent itemsets mining and association rules
• Classification
• Clustering
• Outlier detection

Weitere Voraussetzungen:

Basic knowledge about data sctructures, data engineering and data management.

Prüfungsleistung:

The final exam will be in form of a written exam.

Lehr- und Lernmethoden:

Learning material will be provided in form of presentation slides. Primary lecture media is projected slide presentation. Once in a while complemented with drafts on board/white board.

Verwendbarkeit:

The acquired knowledge and skills can be applied for a Masters thesis.

Literatur:

There is no specific textbook the class's material directly follows. But, most of the class's material is covered in several textbooks:

Primary source:

• Han J., Kamber M., Pei, J., Data Mining: Concepts and Techniques, 3rd ed., Morgan Kaufmann, 2011
• Tan P.-N., Steinbach M., Kumar V., Introduction to Data Mining, Addison-Wesley, 2006.

Further sources:

• Tan, Steinbach, Kumar: Introduction to data mining, Addison Wesley, 2006.
• Liu: Web Data Mining, Springer, 2007.
• Witten, Frank, Hall: Data Mining, Morgan Kaufmann, 2011.

Course content is delivered through slides in PDF formate.

Verweise:

Kommentar: