Data Mining refers to the discovery of previously unknown patterns, structures and correlations in large sets of data. Because the data quantities (big data) are constantly growing, these methods for processing and modeling are particularly valuable because they can identify previously unknown correlations in large data sets.
Possible concepts for identifying patterns range from interactive visualization to classic statistics through to machine learning processes. Methods that are frequently used include classification and regression trees (CART), cluster analyses, regression processes, neuronal networks and forecasting methods with time series data.
One classic problem is the identification of outliers in sensor data or anomaly detection in protocol data. In cluster analysis, groups of similar objects are identified that can be used for error and status typing.
Because data mining has no or very few requirements for carrying out the procedure, validation and critical evaluation of the results are absolutely necessary. Plausibility checks should therefore always be carried out together with machine and domain experts.
The first round of digitalization was won by the United States. Not surprisingly, the primary focus was placed on the service sector. Round two focuses on mechanical engineering, engineering itself and related processes —all core disciplines that are right at home here in Germany. We must seize this opportunity!
Quelle: Dr. Ferri Abolhassan, Deutsche Telekom
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