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Proceedings Paper

Uncertainty in spatial data mining
Author(s): Kun Mei; Yangge Tian; Fulin Bian
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Paper 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.

Paper Details

Date Published: 10 November 2007
PDF: 7 pages
Proc. SPIE 6795, Second International Conference on Space Information Technology, 67956H (10 November 2007); doi: 10.1117/12.775281
Show Author Affiliations
Kun Mei, Wuhan Univ. (China)
Yangge Tian, Wuhan Univ. (China)
Fulin Bian, Wuhan Univ. (China)

Published in SPIE Proceedings Vol. 6795:
Second International Conference on Space Information Technology
Cheng Wang; Shan Zhong; Jiaolong Wei, Editor(s)

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