Spectral clustering methods (Glossary)

Spectral clustering methods are mainly used in the fields of artificial intelligence, big data and smart data as well as Industry and Factory 4.0. This is a method used to group - or "cluster" - large amounts of data according to certain similarities. The special thing about spectral clustering methods is that they recognise complex relationships in the data that other methods often overlook.

Imagine hundreds of sensors in a modern factory collecting data on the temperature, vibration and humidity of machines. Spectral clustering methods can be used to automatically group machines that behave similarly. This allows you to recognise at an early stage if a machine is behaving differently to its "group" - a possible indication of an impending defect.

Compared to traditional methods, spectral clustering analyses the relationships between data points particularly efficiently. This makes it ideal for recognising hidden structures and solving complex problems in industry or when analysing large data sets in the field of artificial intelligence and big data - valuable support for smart decision-making processes.