1996 ADensitybasedAlgorithmforDiscov
- (Ester et al., 1996) ⇒ Martin Ester, Hans-Peter Kriegel, Jörg Sander, and Xiaowei Xu. (1996). “A Density-based Algorithm for Discovering Clusters in Large Spatial Databases with Noise..” In: KDD.
Subject Headings: DBSCAN Algorithm
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Author Keywords
- Clustering Algorithms; Arbitrary Shape of Clusters; Efficiency on Large Spatial Databases; Handling Noise.
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.
References
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 1996 ADensitybasedAlgorithmforDiscov | Martin Ester Hans-Peter Kriegel Xiaowei Xu Jörg Sander | A Density-based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. | 1996 |