Abstract
Association rules are typically evaluated in terms of support and confidence measures, which ensure that discovered rules have enough positive evidence. However, in real-world applications, even considering only those rules with high confidence and support it is not true that all of them are interesting. It may happen that the presentation of all discovered rules can discourage users from interpreting them in order to find nuggets of knowledge. Association rules interpretation can benefit from discovering group of “similar” rules, where (dis)similarity is estimated on the basis of syntactic or semantic characteristics. In this paper, we resort to the multi-dimensional scaling to support a visual exploration of association rules by means of bi-dimensional scatter-plots. An application in the domain of biomedical literature is reported. Results show that the use of this visualization technique is beneficial.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of the 20th International Conference on Very Large Databases (1994)
Batagelj, V., Bren, M.: Comparing resemblance measures. Journal of Classification 12, 73–90 (1995)
Buja, A., Swayne, D.F.: Visualization methodology for multidimensional scaling. Journal of Classification 19, 7–43 (2002)
Cox, T.F., Cox, M.A.A.: Multidimensional Scaling. Chapman and Hall, Boca Raton (1994)
Everitt, B.S., Landau, S., Leese, M.: Cluster Analysis. Edward Arnold (2001)
Klock, H., Buhmann, J.M.: Multidimensional scaling by deterministic annealing. In: Energy Minimization Methods in Computer Vision and Pattern Recognition, pp. 245–260 (1997)
Kruskal, J.: Non-metric multidimensional scaling: a numerical method. Psychometrika 298, 115–129 (1964)
Sammon, J.: A nonlinear mapping for data structure analysis. IEEE Transactions on Computers C-18, 401–409 (1969)
Tsumoto, S., Hirano, S.: Visualization of similarities and dissimilarities in rules using multidimensional scaling. In: Hacid, M.-S., Murray, N.V., Raś, Z.W., Tsumoto, S. (eds.) ISMIS 2005. LNCS (LNAI), vol. 3488, pp. 38–46. Springer, Heidelberg (2005)
Yao, Y.Y., Zhong, N.: An analysis of quantitative measures associated with rules. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 479–488 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Berardi, M., Appice, A., Loglisci, C., Leo, P. (2006). Supporting Visual Exploration of Discovered Association Rules Through Multi-Dimensional Scaling. In: Esposito, F., Raś, Z.W., Malerba, D., Semeraro, G. (eds) Foundations of Intelligent Systems. ISMIS 2006. Lecture Notes in Computer Science(), vol 4203. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875604_43
Download citation
DOI: https://doi.org/10.1007/11875604_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45764-0
Online ISBN: 978-3-540-45766-4
eBook Packages: Computer ScienceComputer Science (R0)