Abstract
Visualization techniques increase the user involvement in the interactive process of data mining and querying of spatio-temporal data. This paper describes a novel geometric approach to clustering and querying of spatio-temporal data. We propose the uniform geometric model based on function representation of solids to cluster and query time-dependent data. Clustering and querying are integrated with visualization techniques in one GUI. First, visual clustering with blobby model allows the user to see the result of clustering on the screen for different time points and/or time intervals and set the appropriate parameters interactively. After that, the user gets the data of clusters for the chosen time frames. Then, the user can visually query the cluster/clusters he/she is interested in with geometric primitive solids which currently are cubes, spheres/ellipsoids, cylinders, etc. Geometric operations of union, intersection and/or subtraction can be performed over the geometric primitive solids to get the final query shape. The user visually clusters spatio-temporal data and queries the clusters with geometric shapes through graphics interface accessing dynamically 3D projections of multidimensional points from database, warehouses or files.With the uniform geometric model of the clustering and querying of spatio-temporal data, 3D visualization tools can be naturally incorporated in one system to allow the user to visualize and query clusters changing over time.
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
Hartigan, J., Wong, M.: A K-means Clustering Algorithm. Applied Statistics 28, 100–108 (1979)
Kaufman, L., Rousseeuw, P.: Finding Groups in Data: A Introduction to Cluster Analysis. John Wiley and Sons, New York (1990)
Guha, S., Rastogi, R., Shim, K.: CURE: A Clustering Algorithm for large databases. Technical report, Bell Laboratories, Murray Hill (1997)
Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In: Proc of KDD 1996 (1996)
Hinneburg, A., Keim, D.A.: An Efficient Approach to Clustering in Large Multimedia Databases with Noise. American Association for Artificial Intelligence (1998)
Fayyad, U.M., Piatetsky-Shapiro, G., Smyyh, P.: From data mining to knowledge discovery: An Overview. In: Advances in Knowledge Discovery and Data Mining, pp. 1–34. MIT Press, Cambridge (1996)
Miller, N.J., Han, J.: Geographic data mining and knowledge discovery: An Overview. In: Geographic Data Mining and Knowledge Discovery, pp. 3–32. Taylor & Fransis, London, New York (2001)
Keim, D.A.: Information Visualization and Visual Data Mining. IEEE Transactions on Visualization and Computer Graphics 8(1), 1–8 (2002)
Sprenger, T.C., Gross, M.H., Eggenberger, A., Kaufmann, M.: A Framework for Physically-Based Information Visualization. In: Proceedings of Eurographics Workshop on Visualization 1997, Boulogne sur Mer, France, April 28-30, pp. 77–86 (1997)
Sprenger, T.C., Brunella, R., Gross, M.H.: H-BLOB: A Hierarchical Visual Clustering Method Using Implicit Surfaces, Department of Computer Science, Swiss Federal Institute of Technology (ETH), Zurich, Switzerland
Hinneburg, A., Keim, D.A., Wawryniuk, M.: HD-Eye: Visual Mining of High-Dimensional Data. IEEE Computer Graphics and Applications, 22–31 (September/October 1999)
Han, J., Kamber, M.: Data Mining Concepts and Techniques. Morgan Kaufmann Publishers, San Francisco (2000)
Güting, R.H., et al.: A Foundation for Representing and Querying Moving Objects. ACM Transaction on Database Systems 25(1), 1–42 (2000)
Erwig, M., Schneider, M.: Developments in Spatio-Temporal Query Languages. In: IEEE Int. Workshop on Spatio-Temporal Data Models and Languages (STDML), pp. 441–449 (1999)
Sourina, O., Boey, S.H.: Geometric Query Types for Data Retrieval in Relational Databases. Data & Knowledge Engineering 27(2), 207–229 (1998)
Sourina, O., Dongquan, L.: Geometric approach to clustering and querying in databases and warehouses. In: Proc. of Cyberworlds 2003, Singapore, December 2003, pp. 326–333 (2003)
Bloomenthal, J.: An Introduction to Implicit Surfaces. Morgan Kaufmann, San Francisco (1997)
Sourina, O., Liu, D.: Visual interactive 3-dimensional clustering with implicit functions. In: Proc. of IEEE CIS 2004 (December 2004)
Ricci, A.: A constructive geometry for computer graphics. The Computer Journal 16(2), 157–160 (1973)
Schroeder, W., Martin, K., Loresen, B.: The Visualization Toolkit. Prentice-Hall, Englewood Cliffs (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sourina, O., Liu, D. (2005). Visual Interactive Clustering and Querying of Spatio-Temporal Data. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2005. ICCSA 2005. Lecture Notes in Computer Science, vol 3483. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11424925_101
Download citation
DOI: https://doi.org/10.1007/11424925_101
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25863-6
Online ISBN: 978-3-540-32309-9
eBook Packages: Computer ScienceComputer Science (R0)