Discovering fuzzy spatial association rules

Esen Kacar; Nihan Kesim Cicekli

doi:10.1117/12.460216

12 March 2002 Discovering fuzzy spatial association rules

Esen Kacar, Nihan Kesim Cicekli

Proceedings Volume 4730, Data Mining and Knowledge Discovery: Theory, Tools, and Technology IV; (2002) https://doi.org/10.1117/12.460216
Event: AeroSense 2002, 2002, Orlando, FL, United States

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.

Citation Download Citation

Esen Kacar and Nihan Kesim Cicekli "Discovering fuzzy spatial association rules", Proc. SPIE 4730, Data Mining and Knowledge Discovery: Theory, Tools, and Technology IV, (12 March 2002); https://doi.org/10.1117/12.460216

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