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| An Improved Spatial Clustering Algorithm |
| HU CaiPing, QIN XiaoLin |
| College of Information Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016 |
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Abstract Spatial clustering is one of the most important spatial data mining techniques. In this paper, an improved spatial clustering algorithm (AISCA) based on DBSCAN is proposed. In order to cluster largescale spatial databases effectively, the proposed algorithm adopts a new sampling technique. In addition, it considers not only spatial attributes but also nonspatial attributes by introducing the concept of the matching neighborhood. Experimental results of 2D spatial datasets show that the proposed algorithm is feasible and efficient.
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Received: 06 April 2006
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2007, Vol. 20 
