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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Koperski, K., Adhikary, J., Han, J.: Spatial Data Mining: Progress and Challenges Survey Paper. In: Proc. ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery, Montreal, Canada (1996)
Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: Clustering for Mining in Large Spatial Databases. Special Issue on Data Mining, KI-Journal 12, 18–24 (1998)
Golfarelli, M., Rizzi, S.: Spatial-Temporal Clustering of Tasks for Swap-Based Negotiation Protocols in Multi-Agent Systems. In: Proceedings 6th International Conference on Intelligent Autonomous Systems, pp. 172–179 (2000)
Galic, S., Loncaric, S., Tesla, E.N.: Cardiac Image segmentation using spatial-temporal clustering. In: Proceedings of SPIE Medical Imaging, San Diego (2001)
Wardlaw, R.L., Frohlich, C., Davis, S.D.: Evaluation of precursory seismic quiescence in sixteen subduction zones using single-link cluster analysis. PAGEOPH:134 (1990)
Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In: Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining (KDD 1996) (1996)
The seismic analysis and forecasting center, China Seismological Bureau, The Seismic Catalog in East of China. The Earthquake Publishing House, Beijing (1980)
The seismic analysis and forecasting center, China Seismological Bureau, The Seismic Catalog in West of China. The Earthquake Publishing House, Beijing (1989)
Beckmann, N., Kriegel, H.P., Schneider, R., Seeger, B.: The R*-tree: An Efficient and Robust Access Method for Points and Rectangles. In: Proc. ACM SIGMOD Int. Conf. On Management of Data, pp. 322–331 (1990)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/11811305_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37025-3
Online ISBN: 978-3-540-37026-0
eBook Packages: Computer ScienceComputer Science (R0)