Clustering trajectories using SOM
نویسندگان
چکیده
Nowadays data acquisition methods continuously record all sort of events occurring in the physical world. Improvements on both hardware and software technologies allow us to collect huge amounts of data, producing, every day, more complete, accurate and detailed pictures of human activity and interaction with the environment. General purpose tools are required for motion patterns, detection of anomalous behavior, motion understanding and prediction. Spatiotemporal data analysis is not a trivial task. One possible analysis is to find objects that have moved in space in a similar way. In this context, trajectory clustering has played an important role in data analysis since it reveals groups of objects with similar moving behaviour.
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