Abstract
A spatial co-location pattern is a group of spatial objects whose instances are frequently located in the same region.The mining co-location pattern problem for certain and uncertain data had been investigated in the past,but not for fuzzy objects.Fuzzy objects could be applied to many areas such as biomedical image databases,GIS and more.This paper investigates the spatial co-location pattern mining problem for fuzzy objects.Firstly,it defines the related concepts of spatial co-location patterns mining on fuzzy objects,including fuzzy participation ratio,fuzzy participation index,etc.Secondly,this paper proposes an FB algorithm to mine co-location patterns from fuzzy objects.Then,three kinds of the improved algorithms,the pruning objects,reducing of the operation joining between spatial instances and optimizing the pruning steps,are put forward so as to improve the mining performance and accelerate the co-location rule generation.Finally,by extensive experiments,the efficiency and effectiveness of the algorithms are verified.
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