Adaptive spatial clustering in the presence of obstacles and facilitators
An intersection-and-combination strategy for clustering spatial point data in the presence of obstacles (e.g. mountain) and facilitators (e.g. highway) is proposed in this paper, and an adaptive spatial clustering algorithm, called ASCDT+, is also developed. The ASCDT+ algorithm can take both obstac...
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| Vydáno v: | Computers & geosciences Ročník 56; s. 104 - 118 |
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01.07.2013
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| Abstract | An intersection-and-combination strategy for clustering spatial point data in the presence of obstacles (e.g. mountain) and facilitators (e.g. highway) is proposed in this paper, and an adaptive spatial clustering algorithm, called ASCDT+, is also developed. The ASCDT+ algorithm can take both obstacles and facilitators into account without additional preprocessing, and automatically detects spatial clusters adjacent to each other with arbitrary shapes and/or different densities. In addition, the ASCDT+ algorithm has the ability to find clustering patterns at both global and local levels so that users can make a more complete interpretation of the clustering results. Several simulated and real-world datasets are utilized to evaluate the effectiveness of the ASCDT+ algorithm. Comparison with two related algorithms, AUTOCLUST+ and DBRS+, demonstrates the advantages of the ASCDT+ algorithm.
•The ASCDT+ algorithm can consider both obstacles (e.g. mountain) and facilitators (e.g. highway).•The ASCDT+ algorithm can detect clusters with different shapes and densities at both global and local levels.•The ASCDT+ algorithm is easy to implement with no need of user-specified parameters. |
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| AbstractList | An intersection-and-combination strategy for clustering spatial point data in the presence of obstacles (e.g. mountain) and facilitators (e.g. highway) is proposed in this paper, and an adaptive spatial clustering algorithm, called ASCDT+, is also developed. The ASCDT+ algorithm can take both obstacles and facilitators into account without additional preprocessing, and automatically detects spatial clusters adjacent to each other with arbitrary shapes and/or different densities. In addition, the ASCDT+ algorithm has the ability to find clustering patterns at both global and local levels so that users can make a more complete interpretation of the clustering results. Several simulated and real-world datasets are utilized to evaluate the effectiveness of the ASCDT+ algorithm. Comparison with two related algorithms, AUTOCLUST+ and DBRS+, demonstrates the advantages of the ASCDT+ algorithm.
•The ASCDT+ algorithm can consider both obstacles (e.g. mountain) and facilitators (e.g. highway).•The ASCDT+ algorithm can detect clusters with different shapes and densities at both global and local levels.•The ASCDT+ algorithm is easy to implement with no need of user-specified parameters. An intersection-and-combination strategy for clustering spatial point data in the presence of obstacles (e.g. mountain) and facilitators (e.g. highway) is proposed in this paper, and an adaptive spatial clustering algorithm, called ASCDT+, is also developed. The ASCDT+ algorithm can take both obstacles and facilitators into account without additional preprocessing, and automatically detects spatial clusters adjacent to each other with arbitrary shapes and/or different densities. In addition, the ASCDT+ algorithm has the ability to find clustering patterns at both global and local levels so that users can make a more complete interpretation of the clustering results. Several simulated and real-world datasets are utilized to evaluate the effectiveness of the ASCDT+ algorithm. Comparison with two related algorithms, AUTOCLUST+ and DBRS+, demonstrates the advantages of the ASCDT+ algorithm. |
| Author | Deng, Min Shi, Yan Liu, Qiliang |
| Author_xml | – sequence: 1 givenname: Qiliang surname: Liu fullname: Liu, Qiliang email: qiliang.liu@connect.polyu.hk organization: Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong – sequence: 2 givenname: Min surname: Deng fullname: Deng, Min email: dengmin028@yahoo.com organization: Department of Surveying and Geo-informatics, Central South University, Changsha, China – sequence: 3 givenname: Yan surname: Shi fullname: Shi, Yan email: shiyan0401060322@126.com organization: Department of Surveying and Geo-informatics, Central South University, Changsha, China |
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| Cites_doi | 10.1109/ICDM.2002.1184042 10.1016/j.isprsjprs.2003.12.003 10.1016/j.cageo.2005.12.004 10.1142/S0218213005002053 10.1109/ICDE.2001.914848 10.1016/j.patrec.2008.01.028 10.1007/3-540-36175-8_56 10.4236/jsea.2010.32018 10.1016/j.compenvurbsys.2011.02.003 10.1016/S0198-9715(01)00044-8 10.1023/A:1015279009755 10.1145/267825.267836 10.1016/j.cageo.2011.12.017 |
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| Keywords | Facilitator Delaunay triangulation Spatial clustering Obstacle Adaptive |
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| References_xml | – reference: Kang, I., Kim, T., Li, K., 1997. A spatial data mining method by Delaunay triangulation. In: Proceedings of 5th ACM Workshop on Advances in Geographic Information Systems. New York, USA, pp. 35–39. – reference: Eldershaw, C., Hegland, M., 1997. Cluster analysis using triangulation. 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