Uncertainty in spatial data mining

Kun Mei; Yangge Tian; Fulin Bian

doi:10.1117/12.775281

10 November 2007 Uncertainty in spatial data mining

Kun Mei, Yangge Tian, Fulin Bian

Proceedings Volume 6795, Second International Conference on Space Information Technology; 67956H (2007) https://doi.org/10.1117/12.775281
Event: Second International Conference on Spatial Information Technology, 2007, Wuhan, China

Abstract

Spatial data mining, i.e., mining knowledge from large amounts of spatial data, is a demanding field since huge amounts of spatial data have been collected in various applications. The collected data far exceeds people's ability to analyze it. Thus, new and efficient methods are needed to discover knowledge from large spatial databases. Most of the spatial data mining methods do not take into account the uncertainty of spatial information. In our work we use objects with broad boundaries, the concept that absorbs all the uncertainty by which spatial data is commonly affected and allows computations in the presence of uncertainty without rough simplifications of the reality. And we propose an uncertainty model that enables efficient analysis of such data. The study case of suitable flounder fishery search indicates the benefit of uncertainty research in spatial data mining.

Citation Download Citation

Kun Mei, Yangge Tian, and Fulin Bian "Uncertainty in spatial data mining", Proc. SPIE 6795, Second International Conference on Space Information Technology, 67956H (10 November 2007); https://doi.org/10.1117/12.775281

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