The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
CLARANS: a method for clustering objects for spatial data mining
Abstract
Spatial data mining is the discovery of interesting relationships and characteristics that may exist implicitly in spatial databases. To this end, this paper has three main contributions. First, it proposes a new clustering method called CLARANS, whose aim is to identify spatial structures that may be present in the data. Experimental results indicate that, when compared with existing clustering methods, CLARANS is very efficient and effective. Second, the paper investigates how CLARANS can handle not only point objects, but also polygon objects efficiently. One of the methods considered, called the IR-approximation, is very efficient in clustering convex and nonconvex polygon objects. Third, building on top of CLARANS, the paper develops two spatial data mining algorithms that aim to discover relationships between spatial and nonspatial attributes. Both algorithms can discover knowledge that is difficult to find with existing spatial data mining algorithms.
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Karypis, G., Han, E.-H., & Kumar, V. (1999) · Computer · doi:10.1109/2.781637 · Cited by 2,097
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
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
Data Mining for Internet of Things: A Survey
Tsai, C.-W., Lai, C.-F., Chiang, M.-C., & Yang, L. T. (2014) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2013.103013.00206 · Cited by 620
Clustering Rules: A Comparison of Partitioning and Hierarchical Clustering Algorithms
Reynolds, A. P., Richards, G., de la Iglesia, B., & Rayward-Smith, V. J. (2006) · Journal of Mathematical Modelling and Algorithms · doi:10.1007/s10852-005-9022-1 · Cited by 578
Metadata
- Authors
- Ng, R. T., & Han, J.
- Venue
- IEEE Transactions on Knowledge and Data Engineering
- Published
- 2002-09
- Volume
- 14
- Issue
- 5
- Pages
- 1003-1016
- ISSN
- 1041-4347 (ISSN-L)
- OpenAlex
- W2126626732
- Cited by
- 1,202
- References
- 32
- FWCI
- 48.22
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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