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An Efficient Trajectory Clustering Framework Based Relative Distance
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along with more and more trajectory dataset being collected into application servers, the research in trajectory clustering has become increasingly important topic. This paper proposes a new mobile object trajectory Clustering algorithm (Trajectory Clustering based Improved Minimum Hausdorff Distance under Translation, TraClustMHD). In this framework, improved Minimum Hausdorff Distance under Translation is presented to measure the similarity between sub-segments. In additional, R-Tree is employed to improve the efficiency. The experimental results showed that this algorithm better than based on Hausdorff distance and based on line Hausdorff distance has good trajectory clustering performance.
Trans Tech Publications, Ltd.
Title: An Efficient Trajectory Clustering Framework Based Relative Distance
Description:
along with more and more trajectory dataset being collected into application servers, the research in trajectory clustering has become increasingly important topic.
This paper proposes a new mobile object trajectory Clustering algorithm (Trajectory Clustering based Improved Minimum Hausdorff Distance under Translation, TraClustMHD).
In this framework, improved Minimum Hausdorff Distance under Translation is presented to measure the similarity between sub-segments.
In additional, R-Tree is employed to improve the efficiency.
The experimental results showed that this algorithm better than based on Hausdorff distance and based on line Hausdorff distance has good trajectory clustering performance.
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