Abstract
Frequent itemset mining is one of the major data mining issues. Once generated by algorithms, the itemsets can be automatically processed, for instance to extract association rules. They can also be explored with visual tools, in order to analyze the emerging patterns. Graphical itemsets representation is a convenient way to obtain an overview of the global interaction structure. However, when the complexity of the database increases, the network may become unreadable. In this paper, we propose to display itemsets on concentric circles, each one being organized to lower the intricacy of the graph through an optimization process. Thanks to a graph bundling algorithm, we finally obtain a compact representation of a large set of itemsets that is easier to exploit. Colors accumulation and interaction operators facilitate the exploration of the new bundle graph and to illustrate how much an itemset is supported by the data.
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
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Bocca, J.B., Jarke, M., Zaniolo, C. (eds.) VLDB 1994, Proc. of 20th Int. Conf. on Very Large Data Bases, Chile, pp. 487–499. Morgan Kaufmann (1994)
Ng, R.T., Lakshmanan, L.V.S., Han, J., Pang, A.: Exploratory mining and pruning optimizations of constrained associations rules. SIGMOD Rec. 27, 13–24 (1998)
Park, J.S., Chen, M.S., Yu, P.S.: Using a hash-based method with transaction trimming for mining association rules. IEEE Trans. on Knowl. and Data Eng. 9, 813–825 (1997)
Han, J., Cercone, N.: Aviz: A visualization system for discovering numeric association rules. In: Terano, T., Liu, H., Chen, A.L.P. (eds.) PAKDD 2000. LNCS, vol. 1805, pp. 269–280. Springer, Heidelberg (2000)
Blanchard, J., Guillet, F., Briand, H.: A user-driven and quality-oriented visualization for mining association rules. In: Proc. of the Third IEEE Int. Conf. on Data Mining, ICDM 2003, pp. 493–496. IEEE Computer Society, Washington (2003)
Leung, C.K.-S., Irani, P.P., Carmichael, C.L.: FIsViz: A frequent itemset visualizer. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 644–652. Springer, Heidelberg (2008)
Singh, V., Garg, D.: Survey of finding frequent patterns in graph mining: Algorithms and techniques. Int. J. of Soft Computing and Engineering 1, 19–23 (2011)
Dickerson, M., Eppstein, D., Goodrich, M.T., Meng, J.Y.: Confluent drawings: Visualizing non-planar diagrams in a planar way. In: Liotta, G. (ed.) GD 2003. LNCS, vol. 2912, pp. 1–12. Springer, Heidelberg (2004)
Holten, D., van Wijk, J.J.: Force-directed edge bundling for graph visualization. Comput. Graph. Forum 28, 983–990 (2009)
Lambert, A., Bourqui, R., Auber, D.: Winding roads: Routing edges into bundles. Comput. Graph. Forum 29, 853–862 (2010)
Telea, A., Ersoy, O.: Image-based edge bundles: simplified visualization of large graphs. In: Proc. of the 12th Eurographics/IEEE - VGTC Conference on Visualization, EuroVis 2010, Aire-la-Ville, Switzerland, pp. 843–852. Eurographics Association (2010)
Holten, D.: Hierarchical edge bundles: Visualization of adjacency relations in hierarchical data. IEEE Transactions on Visualization and Computer Graphics 12, 741–748 (2006)
Cui, W., Zhou, H., Qu, H., Wong, P.C., Li, X.: Geometry-based edge clustering for graph visualization. IEEE Transactions on Visualization and Computer Graphics 14, 1277–1284 (2008)
Gansner, E.R., Hu, Y., North, S., Scheidegger, C.: Multilevel agglomerative edge bundling for visualizing large graphs. In: Proc. of the 2011 IEEE Pacific Visualization Symposium, PacificVis 2011, USA, pp. 187–194. IEEE Computer Society (2011)
Hurter, C., Ersoy, O., Telea, A.: Graph bundling by kernel density estimation. Comp. Graph. Forum 31, 865–874 (2012)
Yang, L.: Visualizing frequent itemsets, association rules, and sequential patterns in parallel coordinates. In: Kumar, V., Gavrilova, M.L., Tan, C.J.K., L’Ecuyer, P. (eds.) ICCSA 2003, Part I. LNCS, vol. 2667, pp. 21–30. Springer, Heidelberg (2003)
Leung, C.K.S., Irani, P.P., Carmichael, C.L.: Wifisviz: Effective visualization of frequent itemsets. In: Proc. of the 2008 Eighth IEEE Int. Conf. on Data Mining, ICDM 2008, USA, pp. 875–880. IEEE Computer Society (2008)
Gansner, E.R., Koren, Y.: Improved circular layouts. In: Kaufmann, M., Wagner, D. (eds.) GD 2006. LNCS, vol. 4372, pp. 386–398. Springer, Heidelberg (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bothorel, G., Serrurier, M., Hurter, C. (2013). Visualization of Frequent Itemsets with Nested Circular Layout and Bundling Algorithm. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2013. Lecture Notes in Computer Science, vol 8034. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41939-3_38
Download citation
DOI: https://doi.org/10.1007/978-3-642-41939-3_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41938-6
Online ISBN: 978-3-642-41939-3
eBook Packages: Computer ScienceComputer Science (R0)