Directional higher order information for spatio-temporal trajectory dataset

Higher order information includes k-nearest neighbor information and k-order region information that are of great importance when the first order or lower order information is not functioning. Despite of the importance of direction in spatio-temporal analysis, directional higher order information has received almost no attention. This paper introduces a new directional higher order information dissimilarity measure that combines topological and geometrical information for spatio-temporal trajectories. It also presents a spider chart-like visualisation approach for directional higher order information and demonstrates the usefulness of this measure with a case study from top-k trajectory mining.

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ICDMW 2014: 14th IEEE International Conference on Data Mining Workshops

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978-1-4799-4275-6

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