Abstract
A composite cluster map displays a fuzzy categorisation of geographic areas. It combines information from several sources to provide a visualisation of the significance of cluster borders. The basic technique renders the chance that two neighbouring locations are members of different clusters as the darkness of the border that is drawn between those two locations. Adding noise to the clustering process is one way to obtain an estimate about how fixed a border is. We verify the reliability of our technique by comparing a composite cluster map with results obtained using multi-dimensional scaling.
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
References
Bertin, J.: Semiologie graphique. Editions Gauthier-Villars, Paris (1967)
Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: A review. ACM Computing Surveys 31, 264–323 (1999)
Nerbonne, J., Heeringa, W., Kleiweg, P.: Edit distance and dialect proximity. In: Sankoff, D., Kruskal, J. (eds.) Time Warps, String Edits and Macromolecules: The Theory and Practice of Sequence Comparison, 2nd edn., pp. v–xv. CSLI, Stanford (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kleiweg, P., Nerbonne, J., Bosveld, L. (2004). Geographic Projection of Cluster Composites. In: Blackwell, A.F., Marriott, K., Shimojima, A. (eds) Diagrammatic Representation and Inference. Diagrams 2004. Lecture Notes in Computer Science(), vol 2980. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25931-2_48
Download citation
DOI: https://doi.org/10.1007/978-3-540-25931-2_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21268-3
Online ISBN: 978-3-540-25931-2
eBook Packages: Springer Book Archive