Summary
Clustering is a classical data analysis technique that is applied to a wide range of applications in the sciences and engineering. For very large data sets, the performance of a clustering algorithm becomes critical. Although clustering has been thoroughly studied over the last decades, little has been done on utilizing modern multi-processor machines to accelerate the analysis process. We propose a scalable clustering technique that benefits from existing parallel computers and networks of workstations. It enables the creation of multiresolution representations for very large geometric data sets. The output of the clustering process can be used for interactive data exploration, supporting techniques like view-dependent rendering, user-guided refinement, or progressive transmission.
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References
Allan D. Gordan, “Hierarchical Classification”, in: P. Arabie, L. J. Hubert, G. De Soete, eds., Clustering and Classification, World Scientific Publ., River Edge, 1996.
Phipps Arabie, Lawrence J. Hubbert, “An Overview of Combinatorial Data Analysis”, in: P. Arabie, L. J. Hubert, G. De Soete, eds., Clustering and Classification, World Scientific Publ., River Edge, 1996.
Richard Franke, Gregory M. Nielson, “Scattered Data Interpolation and Applications: A Tutorial and Survey”, in: Hagen, H. and Roller D., eds., Geometric Modeling, Springer-Verlag, New York, 1991.
Usama Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth, “The KDD Process for Extracting Useful Knowledge from Volumes of Data”, in: Communications of the ACM, 39(11), pp. 27–34, November 1996.
P. Arabie, L. J. Hubert, G. De Soete, “Clustering and Classification”, World Scientific Publ., River Edge, 1996.
M. de Berg, M. van Kreveld, O. Overmars, O. Schwarzkopf, “Computational Geometry - Algorithms and Applications”, Springer-Verlag, Berlin, 1997.
A. Okabe, B. Boots, K. Sugihara, “Spatial Tessellations”, John Wiley and Sons, Chichester, 1992.
R. Cypher, A. Ho, S. Konstantinidou, P. Messina, “A Quantitative Study of Parallel Scientific Applications with Explicit Communications”, Journal of Supercomputing, 10 (1): 5–24, March 1996.
B. Heckel, and B. Hamann. Visualization of cluster hierarchies. In Proceedings of Photonics West Electronic Imaging ’98, SPIE (The International Society for Optical Engineering), San Jose, California, January 1998.
B. Heckel, A. Uva and B. Hamann. Highly effcient generation of hierarchical surface models. In Proceedings of Visualization ’98 (Hot Topics), Wittenbrink and Varshney, Eds., IEEE Computer Society Press, Los Alamitos, CA, Oct 1998, pp. 50–55.
B. Heckel, A. Uva, B. Hamann and Joy. Surface Reconstruction using adaptive clustering methods. Submitted to IEEE Transactions on Visualization and Computer Graphics.
B. Heckel, G. Weber, K. Joy, and B. Hamann. Multiresolution analysis of vector fields. To appear in Proceedings of IEEE Visualization ’99, IEEE Computer Society Press, Los Alamitos, CA, Oct 1999.
G. Weber, B. Heckel, K. Joy, and B. Hamann. Procedural generation of triangulation-based visualizations. To appear in: Proceedings of Visualization ’99 (Hot Topics), A. Varshney, C. M. Wittenbrink, H. Hagen, Eds., IEEE Computer Society Press, Los Alamitos, CA, Oct 1999.
B. Heckel. Clustering-based Multiresolution analysis for Scientific Visualization. Ph.D. thesis, Universite of California, Davis, March 2000.
W. Gropp, Ewing Lusk, and Anthony Skjellum. Using Mpi: Portable Parallel Programming With the Message-Passing Interface. MIT Press, Scientific and Engineering Computation Series, 1994.
Ian T. Foster. Designing and Building Parallel Programs: Concepts and Tools for Parallel Software Engineering. Addison-Wesley, 1994.
P. Lacroute. Real-time volume rendering on shared memory multiprocessors using the shear-warp factorization. In: Cox, M., Uselton, S. P. and Wittenbrink, C. M., Eds., Proc. 1995 Parallel Rendering Symposium, Atlanta, GA, October 30–31, 1995, pp. 15–22.
P. P. Li, W. H. Duquette, D. W. Curkendall. Remote interactive visualization and analysis (RIVA) using parallel supercomputers. In: M. Cox, S. P. Uselton, and C. M. Wittenbrink, eds., Proc. 1995 Parallel Rendering Symposium, Atlanta, GA, October 30–31, 1995, pp. 71–78.
K.-L. Ma. Parallel volume ray-casting for unstructured-grid data on distributed-memory architectures. In: Cox, M., Uselton, S. P. and Wittenbrink, C. M., eds., Proc. 1995 Parallel Rendering Symposium, Atlanta, GA, October 30–31, 1995, pp. 23–30.
K.-L. Ma, J. S. Painter, C. D. Hansen, M. F. Krogh. A data distributed, parallel algorithm for ray-traced volume rendering. In: Crockett, T., Hansen, C. and Whitman, S., eds., Proc. 1993 Parallel Rendering Symposium, San Jose, CA, October 25–26, 1993, pp. 15–22.
U. Neumann. Parallel volume-rendering algorithm performance on mesh-connected multiprocessors. In: Crockett, T., Hansen, C. and Whitman, S., eds., Proc. 1993 Parallel Rendering Symposium, San Jose, CA, October 25–26, 1993, pp. 97–104.
S. Whitman. A load-balanced SIMD polygon renderer. In: M. Cox, S. P. Uselton, and C. M. Wittenbrink, eds., Proc. 1995 Parallel Rendering Symposium, Atlanta, GA, October 30–31, 1995, pp. 63–69.
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Heckel, B., Hamann, B. (2004). Divisive Parallel Clustering for Multiresolution Analysis. In: Brunnett, G., Hamann, B., Müller, H., Linsen, L. (eds) Geometric Modeling for Scientific Visualization. Mathematics and Visualization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-07443-5_21
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DOI: https://doi.org/10.1007/978-3-662-07443-5_21
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