Open AccessProceedings Article
A density-based algorithm for discovering clusters in large spatial Databases with Noise
Martin Ester,Hans-Peter Kriegel,Jörg Sander,Xiaowei Xu +3 more
- pp 226-231
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TLDR
DBSCAN, a new clustering algorithm relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape, is presented which requires only one input parameter and supports the user in determining an appropriate value for it.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.read more
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References
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Finding Groups in Data: An Introduction to Cluster Analysis
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Proceedings ArticleDOI
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Proceedings Article
Efficient and Effective Clustering Methods for Spatial Data Mining
Raymond T. Ng,Jiawei Han +1 more
TL;DR: The analysis and experiments show that with the assistance of CLAHANS, these two algorithms are very effective and can lead to discoveries that are difficult to find with current spatial data mining algorithms.
Journal ArticleDOI
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