A dual distance based spatial clustering method.
Acta Geodaetica et Cartographica Sinica
Most traditional clustering methods only take either the geometric distance or the similarity of attributes into account, splitting the dual characteristics of the spatial features. Thus it is difficult for the clustering results in many practical applications to meet the requirement that the clustered features are both nearest in spatial domain and very similar in attribute domain. So far, some clustering methods which considered dual characteristics of spatial features have many problems, such as algorithm complexity, uncertain clustering results and difficulty for general extension. To solve these problems, this paper proposes a Dual Distance Based Spatial Clustering method (DDBSC), via utilizing the concepts of dual distance reachability and connection. Meanwhile, the algorithm for the implementation of DDBSC is presented and its complexity is further analyzed. Finally, two experiments demonstrate that the DDBSC algorithm is suitable for arbitrary shape of clusters, and is robust for certain magnitude of noise.
|Title:||A dual distance based spatial clustering method|
|Keywords:||Dual distance, Dual distance connected, Dual distance reachable, Spatial clustering|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science
UCL > School of BEAMS > Faculty of Engineering Science > Civil, Environmental and Geomatic Engineering
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