GraphZip: a fast and automatic compression method for spatial data clustering
GraphZip: a fast and automatic compression method for spatial data clustering
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GraphZip：一种快速自动的空间数据聚类压缩方法
DOI：
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发表时间：
2004
期刊：
影响因子：
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通讯作者：
Kang Zhang
中科院分区：
文献类型：
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作者：
Yu Qian;Kang Zhang
由于空间数据的大规模和高维性，空间数据挖掘提出了新的挑战。应对此类挑战的常见方法是对初始数据库执行某种形式的压缩，然后处理压缩后的数据。本文提出了一种新的空间数据压缩方法，称为GraphZip，产生一个紧凑的表示原始数据集。GraphZip有两个优点：首先，原始数据集的空间模式保留在压缩数据中。第二，可以有效地自动处理任意维的数据。将GraphZip应用于大型数据库可以提高空间数据聚类的有效性和效率。一方面，在压缩数据集上执行聚类算法需要更少的运行时间，同时仍然可以发现模式。另一方面，聚类的复杂性大大降低。提出了一种基于GraphZip的通用层次聚类方法。对四个基准空间数据集的实验研究产生了非常令人鼓舞的结果。
Spatial data mining presents new challenges due to the large size and the high dimensionality of spatial data. A common approach to such challenges is to perform some form of compression on the initial databases and then process the compressed data. This paper presents a novel spatial data compression method, called GraphZip, to produce a compact representation of the original data set. GraphZip has two advantages: first, the spatial pattern of the original data set is preserved in the compressed data. Second, arbitrarily dimensional data can be processed efficiently and automatically. Applying GraphZip to huge databases can enhance both the effectiveness and the efficiency of spatial data clustering. On one hand, performing a clustering algorithm on the compressed data set requires less running time while the pattern can still be discovered. On the other hand, the complexity of clustering is dramatically reduced. A general hierarchical clustering method using GraphZip is proposed in this paper. The experimental studies on four benchmark spatial data sets produce very encouraging results.