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一种改进的空间聚类算法*

, PP. 371-376

Keywords: 空间数据挖掘,空间聚类,非空间属性

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Abstract:

空间聚类是空间数据挖掘中一个非常重要的方法.本文在分析DBSCAN算法不足的基础上,提出一种改进的空间聚类算法(AISCA).为了能够有效处理大规模空间数据库,算法采用一种新的抽样技术.另外,通过引入匹配邻域的概念,使得算法在聚类时不仅考虑空间属性也考虑非空间属性.二维空间数据测试结果表明算法是可行、有效的.

References

[1]  Han Jiawei, Kamber M. Data Mining: Concepts and Techniques. Orlando, USA: Morgan Kaufmann Publishers, 2001
[2]  Ng R T, Han Jiawei. CLARANS:A Method for Clustering Objects for Spatial Data Mining. IEEE Trans on Knowledge and Data Engineering, 2002, 14(5): 10031016
[3]  Guha S, Rastogi R, Shim K. CURE: An Efficient Clustering Algorithm for Large Databases // Proc of the ACM SIGMOD International Conference on Management of Data. Seattle, USA, 1998: 7384
[4]  Zhang T, Ramakrishna R, Livny M. BIRCH: An Efficient Data Clustering Method for Very Large Databases // Proc of the ACM SIGMOD International Conference on Management of Data. Montreal, Canada, 1996:103114
[5]  Ester M, Kriegel H, Sander J, et al. A DensityBased Algorithm for Discovering Clusters in Large Spatial Databases with Noise // Proc of the 2nd International Conference on Knowledge Discovery and Data Mining. Portland, USA, 1996: 226231
[6]  Ankerst M, Breunig M, Kriegel H, et al.OPTICS: Ordering Points to Identify the Clustering Structure // Proc of the ACM SIGMOD International Conference on Management of Data Mining. Philadelphia, USA, 1999: 4960
[7]  Sander J, Ester M, Kriegel H, et al. DensityBased Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications. Data Mining and Knowledge Discovery, 1998, 2(2): 169194
[8]  Wang Xin, Hamilton H J. DBRS: A DensityBased Spatial Clustering Method with Random Sampling // Proc of the 7th PacificAsia Conference on Knowledge Discovery and Data Mining. Seoul, Korea, 2003: 563575
[9]  Wang W, Yang J, Muntz R. STING: A Statistical Information Grid Approach to Spatial Data Mining // Proc of the 23rd International Conference on Very Large Data Bases. Athens, Greece, 1997: 186195
[10]  Sheikholeslami G, Chatterjee S, Zhang A. Wave Cluster: A MultiResolution Clustering Approach for Very Large Spatial Databases // Proc of the 24th International Conference on Very Large Data Bases. New York, USA, 1998: 428439
[11]  Beckmann N, Kriegel H P, Schneider R, et al. The R*Tree: An Efficient and Robust Access Method for Points and Rectangles // Proc of the ACM SIGMOD International Conference on Management of Data. Atlantic City, USA, 1990: 322331

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