Abstract
We present a prototype application for graph-based exploration and mining of online databases, with particular emphasis on scientific data. The application builds structured graphs that allow the user to explore patterns in a data set, including clusters, trends, outliers, and relationships. A number of different graphs can be rapidly generated, giving complementary insights into a given data set. The application has a Flash-based graphical interface and uses semantic information from the data sources to keep this interface as intuitive as possible. Data can be accessed from local and remote databases and files. Graphs can be explored using an interactive visual browser, or graph-analytic algorithms. We demonstrate the approach using marine sediment data, and show that differences in benthic species compositions in two Antarctic bays are related to heavy metal contamination.
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References
Washio, T., Motoda, H.: State of the art graph-based data mining. SIGKDD Explorations: Newsletter of the ACM Special Interest Group on Knowledge Discovery & Data Mining 5(1), 59–68 (2003)
Kuramochi, M., Desphande, M., Karypis, G.: Mining Scientific Datasets Using Graphs. In: Kargupta, H., Joshi, A., Sivakumar, K., Yesha, Y. (eds.) Next Generation Data Mining, pp. 315–334. MIT/AAAI Press (2003)
Brieger, R.L.: The analysis of social networks. In: Hardy, M., Bryman, A. (eds.) Handbook of Data Analysis, pp. 505–526. SAGE Publications, London (2004)
Lusseau, D., Newman, M.E.J.: Identifying the role that individual animals play in their social networks. Proceedings of the Royal Society of London B 271, S477–S481 (2004)
Luczkovich, J.J., Borgatti, S.P., Johnson, J.C., Everett, M.G.: Defining and measuring trophic role similarity in food webs using regular equivalence. Journal of Theoretical Biology 220(3), 303–321 (2003)
Yook, S.-H., Oltavai, Z.N., Barabási, A.-L.: Functional and topological characterization of protein interaction networks. Proteomics 4, 928–942 (2004)
De Raedt, L., Kramer, S.: The level wise version space algorithm and its application to molecular fragment finding. In: Proceedings of the Seventeenth International Joint Conference on Articial Intelligence, pp. 853–862. Morgan Kaufmann, San Francisco (2001)
Comiso, J.: Bootstrap sea ice concentrations for NIMBUS-7, SMMR and DMSP SSM/ National Snow and Ice Data Center, I. Boulder, CO, USA (1999, updated 2002)
Global Biodiversity Information Facility, http://www.gbif.net
Swayne, D.F., Buja, A., Temple Lang, D.: Exploratory visual analysis of graphs in GGobi. In: Proceedings of the 3rd International Workshop on Distributed Statistical Computing, Vienna (2003)
Adar, E., Tyler, J.R.: Zoomgraph, http://www.hpl.hp.com/research/idl/projects/graphs/
Winter, A., Kullbach, B., Riediger, V.: An overview of the GXL graph exchange language. In: Diehl, S. (ed.) Dagstuhl Seminar 2001. LNCS, vol. 2269, pp. 324–336. Springer, Heidelberg (2002)
Shapiro, A.: Touchgraph, http://www.touchgraph.com
Cook, D.J., Holder, L.B.: Graph-based data mining. IEEE Intelligent Systems 15(2), 32–41 (2000)
Kuramochi, M., Karypis, G.: Finding frequent patterns in a large sparse graph. In: Berry, M.W., Dayal, U., Kamath, C., Skillicorn, D.B. (eds.) Proceedings of the Fourth SIAM International Conference on Data Mining, Florida, USA. SIAM, Philadelphia (2004)
Cortes, C., Pregibon, D., Volinsky, C.: Computational methods for dynamic graphs. J. Computational and Graphical Statistics 12, 950–970 (2003)
Inokuchi, A., Washio, T., Motoda, H.: Complete mining of frequent patterns from graphs: mining graph data. Machine Learning 50, 321–354 (2003)
Yan, X., Han, J.: CloseGraph: Mining closed frequent graph patterns. In: Getoor, L., Senator, T.E., Domingos, P., Faloutsos, C. (eds.) Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 286–295. ACM, Washington (2003)
Quigley, A., Eades, P.: FADE: graph drawing, clustering, and visual abstraction. In: Marks, J. (ed.) GD 2000. LNCS, vol. 1984, pp. 197–210. Springer, Heidelberg (2001)
Shekhar, S., Lu, C.T., Zhang, P.: Detecting graph-based spatial outliers: algorithms and applications (a summary of results). In: Provost, F., Srikant, R. (eds.) Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 371–376 (2001)
Noble, C.C., Cook, D.J.: Graph-based anomaly detection. In: Getoor, L., Senator, T.E., Domingos, P., Faloutsos, C. (eds.) Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 631–636. ACM, Washington (2003)
Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. USA 99, 7821–7826 (2002)
Drossel, B., McKane, A.J.: Modelling food webs. In: Bornholdt, S., Schuster, H.G. (eds.) Handbook of Graphs and Networks: From the Genome to the Internet, pp. 218–247. Wiley-VCH, Berlin (2003)
Moody, J.: Peer influence groups: identifying dense clusters in large networks. Social Networks 23, 216–283 (2001)
Stark, J.S., Riddle, M.J., Snape, I., Scouller, R.C.: Human impacts in Antarctic marine soft-sediment assemblages: correlations between multivariate biological patterns and environmental variables at Casey Station. Estuarine, Coastal and Shelf Science 56, 717–734 (2003)
Abello, J., Korn, J.: MGV: a system for visualizing massive multi-digraphs. IEEE Transactions on Visualization and Computer Graphics 8, 21–38 (2002)
Wills, G.J.: NicheWorks — interactive visualization of very large graphs. J. Computational and Graphical Statistics 8(2), 190–212 (1999)
Batagelj, V., Mrvar, A.: Pajek - Program for Large Network Analysis, http://vlado.fmf.uni-lj.si/pub/networks/pajek/
Borgatti, S., Chase, R.: UCINET: social network analysis software, http://www.analytictech.com/ucinet.htm
Bongiovanni, B., Choplin, S., Lalande, J.F., Syska, M., Verhoeven, Y.: Mascotte Optimization project, http://www-sop.inria.fr/mascotte/mascopt/index.html
White, S., O’Madadhain, J., Fisher, D., Boey, Y.-B.: Java Universal Network/Graph Framework, http://jung.sourceforge.net
Auber, D.: Tulip — A Huge Graph Visualization Framework, http://www.tulip-software.org/
Adai, A.T., Date, S.V., Wieland, S., Marcotte, E.M.: LGL: creating a map of protein function with an algorithm for visualizing very large biological networks. Journal of Molecular Biology 340(1), 179–190 (2004)
Ellson, J., North, S.: Graphviz - Graph Visualization Software, http://www.graphviz.org/
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Raymond, B., Belbin, L. (2006). Visualisation and Exploration of Scientific Data Using Graphs. In: Williams, G.J., Simoff, S.J. (eds) Data Mining. Lecture Notes in Computer Science(), vol 3755. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11677437_2
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DOI: https://doi.org/10.1007/11677437_2
Publisher Name: Springer, Berlin, Heidelberg
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