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Proceedings Paper

An adaptive spatial clustering algorithm based on the minimum spanning tree-like
Author(s): Min Deng; Qiliang Liu; Guangqiang Li; Tao Cheng
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Paper Abstract

Spatial clustering is an important means for spatial data mining and spatial analysis, and it can be used to discover the potential rules and outliers among the spatial data. Most existing spatial clustering methods cannot deal with the uneven density of the data and usually require predefined parameters which are hard to justify. In order to overcome such limitations, we firstly propose the concept of edge variation factor based upon the definition of distance variation among the entities in the spatial neighborhood. Then, an approach is presented to construct the minimum spanning tree-like (MST-L). Further, an adaptive MST-L based spatial clustering algorithm (AMSTLSC) is developed in this paper. The spatial clustering algorithm only involves the setting of the threshold of edge variation factor as an input parameter, which is easily made with the support of little priori information. Through this parameter, a series of MST-L can be automatically generated from the high-density region to the low-density one, where each MST-L represents a cluster. As a result, the algorithm proposed in this paper can adapt to the change of local density among spatial points. This property is also called the adaptiveness. Finally, two tests are implemented to demonstrate that the AMSTLSC algorithm is very robust and suitable to find the clusters with different shapes. Especially the algorithm has good adaptiveness. A comparative test is made to further prove the AMSTLSC algorithm better than classic DBSCAN algorithm.

Paper Details

Date Published: 15 October 2009
PDF: 9 pages
Proc. SPIE 7492, International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining, 74921Z (15 October 2009); doi: 10.1117/12.838295
Show Author Affiliations
Min Deng, Central South Univ. (China)
Qiliang Liu, Central South Univ. (China)
Guangqiang Li, Central South Univ. (China)
Tao Cheng, Univ. College London (United Kingdom)

Published in SPIE Proceedings Vol. 7492:
International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining
Yaolin Liu; Xinming Tang, Editor(s)

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