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PAKDD
2010
ACM

Correspondence Clustering: An Approach to Cluster Multiple Related Spatial Datasets

9 years 9 months ago
Correspondence Clustering: An Approach to Cluster Multiple Related Spatial Datasets
Domain experts are frequently interested to analyze multiple related spatial datasets. This capability is important for change analysis and contrast mining. In this paper, a novel clustering approach called correspondence clustering is introduced that clusters two or more spatial datasets by maximizing cluster interestingness and correspondence between clusters derived from different datasets. A representative-based correspondence clustering framework and clustering algorithms are introduced. In addition, the paper proposes a novel cluster similarity assessment measure that relies on reclustering techniques and co-occurrence matrices. We conducted experiments in which two earthquake datasets had to be clustered by maximizing cluster interestingness and agreement between the spatial clusters obtained. The results show that correspondence clustering can reduce the variance inherent to representative-based clustering algorithms, which is important for reducing the likelihood of false posi...
Vadeerat Rinsurongkawong, Christoph F. Eick
Added 14 Oct 2010
Updated 14 Oct 2010
Type Conference
Year 2010
Where PAKDD
Authors Vadeerat Rinsurongkawong, Christoph F. Eick
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