Towards mobility-based clustering

@inproceedings{Liu2010TowardsMC,
  title={Towards mobility-based clustering},
  author={Siyuan Liu and Yunhuai Liu and Lionel M. Ni and Jianping Fan and Minglu Li},
  booktitle={KDD},
  year={2010}
}
Identifying hot spots of moving vehicles in an urban area is essential to many smart city applications. The practical research on hot spots in smart city presents many unique features, such as highly mobile environments, supremely limited size of sample objects, and the non-uniform, biased samples. All these features have raised new challenges that make the traditional density-based clustering algorithms fail to capture the real clustering property of objects, making the results less meaningful… CONTINUE READING
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References

Publications referenced by this paper.
Showing 1-5 of 5 references

Achieving anonymity via clustering

ACM Trans. Algorithms • 2010
View 12 Excerpts
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Sliding-window top-k queries on uncertain streams

The VLDB Journal • 2009
View 13 Excerpts
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A Framework for Clustering Uncertain Data Streams

2008 IEEE 24th International Conference on Data Engineering • 2008
View 14 Excerpts
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Density-based clustering for real-time stream data

KDD • 2007
View 4 Excerpts
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