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A Delaunay Triangulation Preprocessing Based Fuzzy-Encroachment Graph Clustering for Large Scale GIS Data

机译:基于Delaunay三角剖分的大规模GIS数据模糊侵占图聚类

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This paper proposed a time efficient graph based spatial clustering for large scale GIS data. As volume of GIS data is large, the direct clustering will not be that much efficient in both space and time complexity domains. So, data is preprocessed using Delaunay Triangulation to reduce both the space and time complexities. The preprocessed data is then considered for spanning tree based crisp clustering. The Fuzzy based postprocessing refinement is used to incorporate some extra points. The time and space complexity has been reduced and as a result efficiency of clustering is achieved.
机译:本文针对大规模GIS数据提出了一种基于时间高效图的空间聚类算法。由于GIS数据量很大,因此在空间和时间复杂性领域中直接聚类的效率都不会很高。因此,使用Delaunay三角剖分对数据进行预处理,以减少空间和时间复杂度。然后考虑将预处理的数据用于基于生成树的明晰聚类。基于模糊的后处理细化用于合并一些额外的点。减少了时间和空间的复杂性,从而实现了聚类的效率。

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