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2011 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies
Clustering and Visualizing Geographic Data Using Geo-tree
Lyon France
August 22-August 27
ISBN: 978-0-7695-4513-4
Plotting lots of geographical data points usually clutters up a map. In this paper, we propose an approach to provide a summary view of geographical data by efficiently clustering. We present a novel data structure, called Geo-tree, which is extended from quad tree, and then develop two algorithms, which use Geo-tree to cluster geographic data and visualize the clusters with a heat map-like representation. The experimental results show that our approach is very efficient in a large scale, compared to K-means and HAC, and the clustering results are comparable to theirs.
Index Terms:
Geo-tree, clustering, visualization, geographic data
Citation:
Che-An Lu, Chin-Hui Chen, Pu-Jen Cheng, "Clustering and Visualizing Geographic Data Using Geo-tree," wi-iat, vol. 1, pp.479-482, 2011 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies, 2011
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