Unüberwachtes Data Mining in Automatisierungs- und Informationstechnik

What it is about

You will gain an overview of established and current methods of unsupervised data mining with a focus on applications in automation and information technology. You will learn to understand various clustering and outlier procedures as well as approaches for high-dimensional feature spaces and data streams, and to critically assess their strengths, limitations, and application conditions.

What you will be able to do

• Explain concepts and fundamental characteristics of unsupervised data mining
• Name and discuss strengths and limitations of unsupervised methods in typical application scenarios
• Understand the basics of conception, implementation and evaluation of unsupervised data mining methods

What the module consists of

• VorlesungDelivery of fundamentals, presentation of application examples and integration of current research results

Teaching method

• Vorlesungsbeiträge mit PowerPointIntroduction to fundamentals and presentation of examples
• Software-DemonstrationenIllustration of the functions and applications of the methods
• DiskussionenClarification of open questions and promotion of active engagement
• Problemorientiertes Lernen (Beispiele)Practice-oriented exercise on conception, implementation and critical evaluation of unsupervised methods
• Vorlesungsfolien zum DownloadSupport for reviewing the content