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基于概念格的空间聚类方法 被引量:2

A Spatial Clustering Method Based on Concept Lattices
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摘要 空间聚类一直是空间数据挖掘研究的热点之一。现有的聚类方法大都局限于根据空间位置来进行空间聚类的,忽略了空间对象的专题属性,从而导致空间聚类结果有时完全不符合人的空间认知,缺乏合理的解释。为此,综合考虑空间对象的位置和专题属性,提出了一种基于概念格的空间聚类(Concept Lattices Based SpatialCluster,CLBSC)方法。该方法通过构建多维专题属性的概念格,简化了空间聚类计算。最后,通过两组实验对CLBSC算法进行了验证分析,研究结果表明:所提出的CLBSC算法是一种具有高可靠性和抗噪性的空间聚类算法。 Spatial clustering is a hot issue in the field of spatial data mining. For a spatial object, the spatial location and the thematic attributes of spatial data are the inherent characteristics. However, the existing approaches mostly regard only the distance of spatial location as the similarity metric of spatial clustering, ignoring the thematic attributes of spatial objects. The results of these spatial clustering methods are not reasonable. Thus, a new spatial clustering method, named Concept Lattices Based Spatial Cluster (CLBSC for short) is proposed in this paper. The method considers both the spatial distance and attribute distance, and it simplifies the computation via building multi-dimensional attribute lattices. Furthermore, many concepts about CLBSC are expounded and its algorithm is narrated in detain. Finally, two experiments demonstrate that CLBSC algorithm is able to find more outlier and improve the reliability of spatial clustering using the Same Lattices Number.
出处 《计算机系统应用》 2011年第6期103-108,共6页 Computer Systems & Applications
基金 国家高技术研究发展计划(863)(2009AA12Z206) 湖南省自然科学基金(09JJ6061)
关键词 空间聚类 概念格 空间位置属性:专题属性 CLBSC spatial clustering, concept lattices, spatial location thematic attributes CLBSC
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