基于Delaunay三角剖分预处理的大尺度GIS模糊入侵图聚类

Parthajit Roy, J. K. Mandal
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引用次数: 8

摘要

提出了一种基于图的大尺度GIS数据空间聚类方法。由于地理信息系统数据量大,直接聚类在空间和时间复杂度域的效率都不高。因此,使用Delaunay三角剖分法对数据进行预处理,以减少空间和时间的复杂性。然后将预处理后的数据用于基于生成树的清晰聚类。采用基于模糊的后处理细化,加入了一些额外的点。降低了聚类的时间和空间复杂度,提高了聚类的效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Delaunay Triangulation Preprocessing Based Fuzzy-Encroachment Graph Clustering for Large Scale GIS Data
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.
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