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The parity mode spatial data mining algorithms in GIS

Author: TanHao
Tutor: ZhangXiuRu
School: Central South University
Course: Information and Communication Engineering
Keywords: Spatial Data Mining The same bit pattern KD-Tree apriori-gen Density
CLC: TP311.13
Type: Master's thesis
Year: 2011
Downloads: 48
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Abstract


Spatial data mining is the extraction of implicit knowledge, spatial relationships , or stored in a database meaningful features and patterns from the spatial database , it is an important research direction of data mining , with the growing development of spatial database technology and spatial data increasingly highlight the importance of the accumulation of spatial data mining technology , the same bit pattern in spatial data mining mining algorithms were elaborated and research , summed up the tradition with the characteristics and limitations of the mining algorithms , and on this basis to make improvements. The traditional method of spatial association rules in the processing of spatial relationships is not applicable , the same bit pattern proposed to solve the need for mining the correct spatial association rules . The space of the same bit pattern , is the collection of some of the spatial characteristics of these the spatial characteristics instance because spatial proximity frequently together . KD - Tree of parity mode mining algorithm , is an efficient space index structure introduced on the basis of of traditional parity mode mining , KD-Tree to search to meet the spatial neighborhood relations of the instance to generate all of the two-dimensional parity mode , re-use connection apriori-gen algorithm steps to generate the same bit more than the two-dimensional model to improve the efficiency of the algorithm ; in this basis , combined with the uneven distribution of spatial data in the real world , a density-based same bit pattern mining algorithm , partitions and introducing through the space involved in the index of the dynamic upper bounds , do not have to be confirmed by the detection one by one for all of the characteristics of instances in the entire space , and reduce the time of detection of a neighborhood relationship , as well as the number of concatenation . Finally, this paper analyzes the correctness and completeness of the algorithm , the algorithm through experiments to simulate the experimental results show that the improved algorithm proposed in this paper improve operating efficiency than previous algorithms . Meanwhile, this paper the component GIS achieve the same bit pattern mining based on GIS platform space module .

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