Temporal and Spatial Data Mining

The lecture deals with fundamentals of pattern recognition in time series (e.g., sensor signals) and spatially distributed data (e.g., in sensor networks). There are u.a. the following topics were discussed:

Basics (e.g., segmentation of time series, correlation of data, characteristics for description temporal / spatial data),

Distance measurement of time series, clustering / classification, scene recognition, anomaly detection using various techniques (e.g. nearest neighbour, neural networks, support vector machines)

Various example applications (signature verification, collaborative hazard warning in vehicles, activity detection and context recognition with smartphones, etc.)