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ZHOU Shui Geng, ZHOU Ao Ying, CAO Jing, and HU Yun Fa (Department of Computer Science, Fudan University, Shanghai 200433)  
Clustering is a promising application area for many fields including data mining, statistical data analysis, pattern recognition, image processing, etc. In this paper, a fast density based clustering algorithm is developed, which considerably speeds up the original DBSCAN algorithm. Unlike DBSCAN, the new DBSCAN uses only a small number of representative objects in a core object's neighborhood as seeds to expand the cluster so that the execution frequency of region query can be decreased, and consequently the I/O cost is reduced. Experimental results show that the new algorithm is effective and efficient in clustering large scale databases, and it is faster than the original DBSCAN by several times.
【Fund】: 国家自然科学基金项目!(项目编号 6 97430 0 1);; 国家教委博士点教育基金
【CateGory Index】: TP311
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