Detecting and tracking dynamic clusters of spatial events

Article English OPEN
Andrienko, N. ; Andrienko, G. ; Fuchs, G. ; Stange, H. (2014)

We present a work in progress on developing a tool supporting real-time detection of significant clusters of spatial events and observing their evolution. The tool consists of an incremental stream clustering algorithm and coordinated map and timeline displays showing current situation and cluster evolution.
  • References (4)

    [1] 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.

    [2] 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.

    [3] Andrienko, N., Andrienko, G.: Spatial generalization and aggregation of massive movement data. IEEE Trans. Visualization and Computer Graphics, 17(2): 205-219, 2011.

    [4] 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.

  • Metrics
    0
    views in OpenAIRE
    0
    views in local repository
    25
    downloads in local repository

    The information is available from the following content providers:

    From Number Of Views Number Of Downloads
    City Research Online - IRUS-UK 0 25
Share - Bookmark