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Association Rule Visualization and Pruning through Response-Style Data Organization and Clustering

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNAI,volume 7637)

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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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

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34653-8

  • Online ISBN: 978-3-642-34654-5

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