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A distribution-based clustering algorithm for mining in large spatial databases
Abstract
The problem of detecting clusters of points belonging to a spatial point process arises in many applications. In this paper, we introduce the new clustering algorithm DBCLASD (Distribution-Based Clustering of LArge Spatial Databases) to discover clusters of this type. The results of experiments demonstrate that DBCLASD, contrary to partitioning algorithms such as CLARANS (Clustering Large Applications based on RANdomized Search), discovers clusters of arbitrary shape. Furthermore, DBCLASD does not require any input parameters, in contrast to the clustering algorithm DBSCAN (Density-Based Spatial Clustering of Applications with Noise) requiring two input parameters, which may be difficult to provide for large databases. In terms of efficiency, DBCLASD is between CLARANS and DBSCAN, close to DBSCAN. Thus, the efficiency of DBCLASD on large spatial databases is very attractive when considering its nonparametric nature and its good quality for clusters of arbitrary shape.
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Cited by
A Comprehensive Survey of Clustering Algorithms
Xu, D., & Tian, Y. (2015) · Annals of Data Science · doi:10.1007/s40745-015-0040-1 · Cited by 1,939
Algorithms for hierarchical clustering: an overview
Murtagh, F., & Contreras, P. (2012) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.53 · Cited by 1,874
ST-DBSCAN: An algorithm for clustering spatial–temporal data
Birant, D., & Kut, A. (2007) · Data & Knowledge Engineering · doi:10.1016/j.datak.2006.01.013 · Cited by 1,430
A Survey of Clustering Algorithms for Big Data: Taxonomy and Empirical Analysis
Fahad, A., Alshatri, N., Tari, Z., Alamri, A., Khalil, I., Zomaya, A. Y., Foufou, S., & Bouras, A. (2014) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2014.2330519 · Cited by 1,042
Entropy-based subspace clustering for mining numerical data
Cheng, C.-H., Fu, A. W., & Zhang, Y. (1999) · Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/312129.312199 · Cited by 543
Metadata
- Authors
- Xu, X., Ester, M., Kriegel, H.-P., & Sander, J.
- Venue
- Proceedings 14th International Conference on Data Engineering
- Publisher
- IEEE Comput. Soc
- Published
- 2002-11-27
- Pages
- 324-331
- ISSN
- 1084-4627 (ISSN-L)
- OpenAlex
- W2166933393
- Cited by
- 352
- References
- 0
- FWCI
- 12.51
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Xu, X., Ester, M., Kriegel, H.-P., & Sander, J. (2002). A distribution-based clustering algorithm for mining in large spatial databases. Proceedings 14th International Conference on Data Engineering. https://doi.org/10.1109/icde.1998.655795
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