WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases
WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases
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1998-08
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通讯作者：
Gholamhosein Sheikholeslami;Surojit Chatterjee;A. Zhang
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作者：
Gholamhosein Sheikholeslami;Surojit Chatterjee;A. Zhang
许多应用都需要对空间数据进行管理。对大型空间数据库进行聚类是一个重要的问题，它试图在特征空间中找到人口密集的区域，以便用于数据挖掘、知识发现或高效的信息检索。一个好的聚类方法应该是高效的，并能检测出任意形状的簇。它必须对异常值(噪声)和输入数据的顺序不敏感。我们提出了一种新的基于小波变换的聚类方法--WaveClusion，它满足了上述要求。利用小波变换的多分辨率特性，我们可以有效地识别出不同精度的任意形状的簇。我们还证明了WaveCluster在时间复杂度方面是高效的。在超大数据集上的实验结果表明，该方法与现有的其他聚类方法相比具有较高的效率和有效性。本研究得到了施乐公司的支持。允许免费复制本材料的全部或部分，前提是复制不是为了直接商业利益而制作或分发，VLDB版权声明和出版物的标题及其日期，并且通知复制是经过Very Large数据库捐赠的许可。以其他方式复制或重新出版，需要获得捐赠基金的费用和/或特别许可。第24届VLDB会议论文集，美国纽约
Many applications require the management of spatial data. Clustering large spatial databases is an important problem which tries to find the densely populated regions in the feature space to be used in data mining, knowledge discovery, or efficient information retrieval. A good clustering approach should be efficient and detect clusters of arbitrary shape. It must be insensitive to the outliers (noise) and the order of input data. We propose WaveCluster, a novel clustering approach based on wavelet transforms, which satisfies all the above requirements. Using multiresolution property of wavelet transforms, we can effectively identify arbitrary shape clusters at different degrees of accuracy. We also demonstrate that WaveCluster is highly efficient in terms of time complexity. Experimental results on very large data sets are presented which show the efficiency and effectiveness of the proposed approach compared to the other recent clustering methods. This research is supported by Xerox Corporation. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and/or special permission from the Endowment. Proceedings of the 24th VLDB Conference New York, USA, 1998