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2004

Clustering Spatial Data in the Presence of Obstacles

8 years 11 months ago
Clustering Spatial Data in the Presence of Obstacles
Clustering is a form of unsupervised machine learning. In this paper, we proposed the DBRS_O method to identify clusters in the presence of intersected obstacles. Without doing any preprocessing, DBRS_O processes the constraints during clustering. DBRS_O can also avoid unnecessary computations when obstacles do not affect the clustering result. As well, DBRS_O can find clusters with arbitrary shapes, varying densities, and significant non-spatial attributes in large datasets.
Xin Wang, Howard J. Hamilton
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2004
Where FLAIRS
Authors Xin Wang, Howard J. Hamilton
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