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For applications of clustering algorithms, the key techniques are to handle complicatedly distributed clusters and process massive data effectively and efficiently. On the basis of analysis and research of traditional clustering algorithms, a clustering algorithm based on density and adaptive density-reachable is presented in this paper, which can handle clusters of arbitrary shapes, sizes and densities...
Many applications require the management of spatial data in very large spatial databases. We propose a grid-based clustering algorithm to process spatial data. This algorithm uses a grid-based method to identify data that are compressible and data that must be maintained in memory. Thus the algorithm compresses data without decreasing the quality of clustering. Through one scan over a database, our...
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