Document - Real Time Detection and Tracking of Spatial Event Clusters

2015

Conference article Open Access

Real Time Detection and Tracking of Spatial Event Clusters

Andrienko N., Andrienko G., Fuchs G., Rinzivillo S., Betz H. D.

Event detection QA75 Clustering

We demonstrate a system of tools for real-time detection of significant clusters of spatial events and observing their evolution. The tools include an incremental stream clustering algorithm, interactive techniques for controlling its operation, a dynamic map display showing the current situation, and displays for investigating the cluster evolution (time line and space-time cube).

Source: Machine Learning and Knowledge Discovery in Databases. European Conference, pp. 316–319, Porto, Potugal, 07-11/09/2015

Publisher: Springer, Berlin , Germania

Citations

Aggarwal, C.C., Han, J., Wang, J., Yu, P.S.: A framework for clustering evolving data streams, in: Proc. 29th Int. Conf. Very Large Data Bases, Berlin, Germany, 2003.
Cao, F., Ester, M., Qian, W., Zhou, A.: Density-based clustering over an evolving data stream with noise, in: Proc. 6th SIAM Int. Conf. Data Mining, SIAM, Bethesda, Maryland, USA, 2006.
Andrienko, N., Andrienko, G.: Spatial generalization and aggregation of massive movement data. IEEE Trans. Visualization and Computer Graphics, 17(2): 205-219, 2011.
Bouguelia M.-R., Belaïd Y., Belaïd, A.: An Adaptive Incremental Clustering Method Based on the Growing Neural Gas Algorithm, in: Proc. 2nd Int. Conf. Pattern Recognition Applications and Methods - ICPRAM 2013, 42-49, 2013.

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BibTeX entry

@inproceedings{oai:it.cnr:prodotti:346091,
	title = {Real Time Detection and Tracking of Spatial Event Clusters},
	author = {Andrienko N. and Andrienko G. and Fuchs G. and Rinzivillo S. and Betz H. D.},
	publisher = {Springer, Berlin , Germania},
	doi = {10.1007/978-3-319-23461-8_38},
	booktitle = {Machine Learning and Knowledge Discovery in Databases. European Conference, pp. 316–319, Porto, Potugal, 07-11/09/2015},
	year = {2015}
}