Abstract
Association rules are a very popular non-supervised data mining technique for extracting co-relation in large set of data transactions. Although the vast use, the analysis of mined rules may be intricate for non-experts, and the technique effectiveness is constrained by the data dimensionality. This paper presents a pre-processing approach that uses (1) dual scaling in order to present the mined rules with some semantic contextualization that assists interpretation, and (2) mean shift clustering to reduce data dimensionality. We tested our model with real data collected from accident reports in petroleum industry.
Keywords
- data mining
- Apriori
- association rules
- pruning
- dimension reduction
- clustering
- dual scaling
- mean shift
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References
Agrawal, R., Imielinski, T., Swami, A.: Mining Associations Between Sets of Items in Massive Databases. In: 1993 ACM SIGMOD Intl. Conf. on Management of Data (SIGMOD 1993), pp. 207–216. ACM (1993)
Bayardo, R., Agrawal, R.: Mining the Most Interesting Rules. In: 5th ACM SIGKDD Intl. Conf. on Know. Dis. & Data Mining (KDD 1999), pp. 145–154 (1999)
Bayardo, R., Agrawal, R., Gunopulos, D.: Constraint-Based Rule Mining in Large, Dense Databases. Data Mining and Knowledge Discovery 4(2-3), 217–240 (2000)
Berrado, A., Runger, G.C.: Using Metarules to Organize and Group Discovered Association Rules. Data Mining and Knowledge Discovery 14(3), 409–431 (2007)
Bruzzese, D., Davino, C.: Visual Mining of Association Rules. In: Simoff, S.J., Böhlen, M.H., Mazeika, A. (eds.) Visual Data Mining. LNCS, vol. 4404, pp. 103–122. Springer, Heidelberg (2008)
Buono, P., Costabile, M.F.: Visualizing Association Rules in a Framework for Visual Data Mining. In: Hemmje, M., Niederée, C., Risse, T. (eds.) From Integrated Publication and Information Systems to Information and Knowledge Environments. LNCS, vol. 3379, pp. 221–231. Springer, Heidelberg (2005)
Comaniciu, D., Ramesh, V., Meer, P.: The Variable Bandwidth Mean Shift and Data-Driven Scale Selection. In: 8th IEEE Intl. Conf. on Computer Vision (ICCV 2001), pp. 438–445. IEEE (2001)
Domingues, M.A., Rezende, S.O.: Post-Processing of Association Rules Using Taxonomies. In: 12th Portuguese Conf. on Artif. Intel. (EPIA 2005). pp. 192–197 (2005)
Einbeck, J.: Bandwidth Selection for Mean-Shift Based Unsupervised Learning Techniques – A Unified Approach Via Self-Coverage. Journal of Pattern Recognition Research 6(2), 175–192 (2011)
Fukunaga, K., Hostetler, L.: The Estimation of the Gradient of a Density Function, with Applications in Pattern Recognition. IEEE Trans. on Information Theory 21(1), 32–40 (1975)
Garcia, A.C.B., Ferraz, I., Vivacqua, A.S.: From Data to Knowledge Mining. Artif. Intel. for Eng. Design, Analysis and Manufacturing 23(4), 427–441 (2009)
Hofmann, H., Siebes, A., Wilhelm, A.: Visualizing Association Rules with Interactive Mosaic Plots. In: 6th ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining (KDD 2000), pp. 227–235. ACM (2000)
Lavrac, N., Flach, P., Zupan, B.: Rule Evaluation Measures – A Unifying View. In: Džeroski, S., Flach, P. (eds.) ILP 1999. LNCS (LNAI), vol. 1634, pp. 174–185. Springer, Heidelberg (1999)
Marinica, C., Guillet, F.: Filtering Discovered Association Rules Using Ontologies. IEEE Trans. on Knowledge and Data Eng. 22(6), 784–797 (2009)
Nishisato, S.: Elements of Dual Scaling – An Introduction to Practical Data Analysis. Psychology Press (1993)
Nishisato, S.: On Quantifying Different Types of Categorical Data. Psychometrika 58(4), 617–629 (1993)
Nishisato, S.: Gleaning in the Field of Dual Scaling. Psychometrika 61(4), 559–599 (1996)
Shekar, B., Natarajan, R.: A Framework for Evaluating Knowledge-Based Interestingness of Association Rules. Fuzzy Opt. and Dec. Making 3(2), 157–185 (2004)
Srikant, R., Agrawal, R.: Mining Generalized Association Rules. In: 21st Intl. Conf. on Very Large Databases (VLDB 1995), pp. 407–419. Morgan Kaufmann (1995)
Tan, P., Kumar, V., Srivastava, J.: Selecting the Right Objective Measure for Association Analysis. Information Systems 29(4), 293–313 (2004)
Wang, J., Thiesson, B., Xu, Y., Cohen, M.: Image and Video Segmentation by Anisotropic Kernel Mean Shift. In: Pajdla, T., Matas, J. (eds.) ECCV 2004. LNCS, vol. 3022, pp. 238–249. Springer, Heidelberg (2004)
Yang, L.: Visualizing Frequent Itemsets, Association Rules, and Sequential Patterns in Parallel Coordinates. In: Kumar, V., Gavrilova, M.L., Tan, C.J., L’Ecuyer, P. (eds.) ICCSA 2003, Part I. LNCS, vol. 2667, pp. 21–30. Springer, Heidelberg (2003)
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Fernandes, L.A.F., García, A.C.B. (2012). Association Rule Visualization and Pruning through Response-Style Data Organization and Clustering. In: Pavón, J., Duque-Méndez, N.D., Fuentes-Fernández, R. (eds) Advances in Artificial Intelligence – IBERAMIA 2012. IBERAMIA 2012. Lecture Notes in Computer Science(), vol 7637. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34654-5_8
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DOI: https://doi.org/10.1007/978-3-642-34654-5_8
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