Refereed full papers (journals, book chapters, international conferences)

1996

@inproceedings{EKS1996,
	vgclass =	{refpap},
	author =	{Martin Ester and Hans-Peter Kriegel and J\"{o}rg Sander and Xiaowei Xu},
	title =	{A Density-Based Algorithm for Discovering Clusters 
	in Large Spatial Databases with Noise},
	editor =	{Evangelos Simoudis and Jiawei Han, and Usama Fayyad},
	booktitle =	{Proceedings of the 2nd International Conference on Knowledge
	Discovery and Data Mining (KDD'96)},
	address =	{Portland, OR, USA},
	pages =	{226--231},
	month =	{August~2--4},
	year =	{1996},
	url =	{http://www.cs.ualberta.ca/\~{}joerg/papers/KDD-96_final.pdf},
	abstract =	{Clustering algorithms are attractive for the task of class
	identification in spatial databases. However, the application to 
	large spatial databases rises the following requirements for 
	clustering algorithms: minimal requirements of domain 
	knowledge to determine the input parameters, discovery of 
	clusters with arbitrary shape and good efficiency on large databases. The well-known clustering algorithms offer no solution to the combination of these requirements. In this paper, 
	we present the new clustering algorithm DBSCAN relying on 
	a density-based notion of clusters which is designed to discover clusters of arbitrary shape. DBSCAN requires only one 
	input parameter and supports the user in determining an appropriate value for it. We performed an experimental evaluation of the effectiveness and efficiency of DBSCAN using 
	synthetic data and real data of the SEQUOIA 2000 benchmark. The results of our experiments demonstrate that (1) 
	DBSCAN is significantly more effective in discovering clusters of arbitrary shape than the well-known algorithm CLARANS, and that (2) DBSCAN outperforms CLARANS by a 
	factor of more than 100 in terms of efficiency.},
}