Ambiguity Within The Scope Of Threshold Co - The Location Of Fuzzy Object Pattern Mining

Space co-location patterns represent a group of spatial objects whose instances are frequently associated in the space. Space co-location pattern mining is an important research direction for spatial data mining, and has a very wide range of applications in real lift. The mining co-location pattern problem for certain and uncertain data had been investigated in the past, but not for fuzzy data. Fuzzy data could be applied to many areas such as GIS and biomedical image databases. This paper investigates the spatial co-location pattern mining problem for ambiguity range.First, the definitions, theorems, related work of the spatial co-location pattern mining and algorithms are introduced.Second, we investigate the spatial co-location pattern mining problem for fuzzy objects. The related concepts and algorithms of spatial co-location patterns mining on fuzzy objects are proposed. Algorithms includes FB algorithm, the pruning objects, reducing of the operation joining between spatial instances, optimizing the pruning steps and grid-based distance calculation. By extensive experiments, we analyzed the performance and results of the algorithms.Third, because the spatial co-location pattern mining for fuzzy objects only in a single probability threshold, so the paper investigates the problem for ambiguity range. The related concepts are defined, and on this basis, a basic algorithm has been proposed. To improve the mining performance, two kinds of the improved algorithms---reducing the number of mining and narrowing the excavation area are put forward. The time performance of the algorithms has been analyzed.Forth, the fuzzy object co-location pattern mining applied in a "three parallel rivers" project.At the last, conclusion and future work were presented.