Abstract
In order to mine spatial-temporal clusters from geo-databases, two clustering methods with close relationships are proposed, which are both based on neighborhood searching strategy, and rely on the sorted k-dist graph to automatically specify their respective algorithm arguments. We declare the most distinguishing advantage of our clustering methods is they avoid calculating the spatial-temporal distance between patterns which is a tough job. Our methods are validated with the successful extraction of seismic sequence from seismic databases, which is a typical example of spatial–temporal clusters.
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Wang, M., Wang, A., Li, A. (2006). Mining Spatial-temporal Clusters from Geo-databases. In: Li, X., Zaïane, O.R., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science(), vol 4093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811305_29
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DOI: https://doi.org/10.1007/11811305_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37025-3
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