Machine Learning for Spatio-Temporal Data Mining @ IPLAB

The aim of this page is to present research done by the Image Processing LABoratory(IPLAB) in the scope of Machine Learning for Spatio-Temporal Data Mining.

The term spatio-temporal data mining refers to the methods that extract patterns from the spatio-temporal data in order to increase the accuracy of a classification engine. The spatial data mining field has grown incredibly over the past years. This is due to the limitations of the classical data mining approaches (like for instance high computational costs, poor performance in case of incomplete data). The main advantage of spatial data mining methods is that they allow to understand the complex relationships between spatial data in order to turn these into useful information. In presence of temporal sequences of data, further exploitation applying Machine Learning techniques is crucial to obtain effective results. More specifically, we focus our attention on the analysis of spatio-temporal data in various fields: quantitative finance, healthcare, automotive, social media, etc.