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Fuzzy Set Approaches to Spatial Data Mining of Association Rules
Author(s) -
Ladner R,
Petry F E,
Cobb M A
Publication year - 2003
Publication title -
transactions in gis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.721
H-Index - 63
eISSN - 1467-9671
pISSN - 1361-1682
DOI - 10.1111/1467-9671.00133
Subject(s) - data mining , association rule learning , fuzzy logic , spatial analysis , fuzzy set , computer science , data set , set (abstract data type) , association (psychology) , artificial intelligence , mathematics , statistics , philosophy , epistemology , programming language
This paper presents an approach to the discovery of association rules for fuzzy spatial data. Association rules provide information of value in assessing significant correlations that can be found in large databases. Here we are interested in correlations of spatially related data such as soil types, directional or geometric relationships, etc. We have combined and extended techniques developed in both spatial and fuzzy data mining in order to deal with the uncertainty found in typical spatial data.