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Proceedings of the 8th ACM SIGSPATIAL Workshop on GeoStreaming, 2017
An unsupervised methodology is presented for the detection of motion anomalies using spatial context and multivariate statistical tests. The method is applied to GPS data captured for a taxi fleet in Porto, Portugal; and, AIS data captured for ships operating in the Aegean Sea.
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An unsupervised methodology is presented for the detection of motion anomalies using spatial context and multivariate statistical tests. The method is applied to GPS data captured for a taxi fleet in Porto, Portugal; and, AIS data captured for ships operating in the Aegean Sea.
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2005
Preparing a dataset is a very important step in data mining. If the input to the process contains problems, noise, or errors, then the results will reflect this, as well. Not all possible combinations of the data should exist, as the data represent real-world observations. Correlation is expected among the variables.
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Preparing a dataset is a very important step in data mining. If the input to the process contains problems, noise, or errors, then the results will reflect this, as well. Not all possible combinations of the data should exist, as the data represent real-world observations. Correlation is expected among the variables.
openaire +1 more source