12 March 2002 Discovering fuzzy spatial association rules
Author Affiliations +
Abstract
Discovering interesting, implicit knowledge and general relationships in geographic information databases is very important to understand and use these spatial data. One of the methods for discovering this implicit knowledge is mining spatial association rules. A spatial association rule is a rule indicating certain association relationships among a set of spatial and possibly non-spatial predicates. In the mining process, data is organized in a hierarchical manner. However, in real-world applications it may not be possible to construct a crisp structure for this data, instead some fuzzy structures should be used. Fuzziness, i.e. partial belonging of an item to more than one sub-item in the hierarchy, could be applied to the data itself, and also to the hierarchy of spatial relations. This paper shows that, strong association rules can be mined from large spatial databases using fuzzy concept and spatial relation hierarchies.
© (2002) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Esen Kacar, Esen Kacar, Nihan Kesim Cicekli, Nihan Kesim Cicekli, } "Discovering fuzzy spatial association rules", Proc. SPIE 4730, Data Mining and Knowledge Discovery: Theory, Tools, and Technology IV, (12 March 2002); doi: 10.1117/12.460216; https://doi.org/10.1117/12.460216
PROCEEDINGS
9 PAGES


SHARE
RELATED CONTENT

Association rule mining based on concept lattice
Proceedings of SPIE (December 02 2005)
A data mining algorithm based on the rough sets theory...
Proceedings of SPIE (December 02 2005)
Immune algorithm for KDD
Proceedings of SPIE (September 24 2001)
Efficiently mining maximal frequent patterns: fast-miner
Proceedings of SPIE (March 26 2001)
User profiling in WWW network
Proceedings of SPIE (February 22 2005)

Back to Top