ARVis

Interactive visual exploration of association rules with rule-focusing methodology. On account of the enormous amounts of rules that can be produced by data mining algorithms, knowledge post-processing is a difficult stage in an association rule discovery process. In order to find relevant knowledge for decision making, the user (a decision maker specialized in the data studied) needs to rummage through the rules. To assist him/her in this task, we here propose the rule-focusing methodology, an interactive methodology for the visual post-processing of association rules. It allows the user to explore large sets of rules freely by focusing his/her attention on limited subsets. This new approach relies on rule interestingness measures, on a visual representation, and on interactive navigation among the rules. We have implemented the rule-focusing methodology in a prototype system called ARVis. It exploits the user’s focus to guide the generation of the rules by means of a specific constraint-based rule-mining algorithm.


References in zbMATH (referenced in 8 articles )

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  1. Mihelčić, Matej; Šmuc, Tomislav: Targeted and contextual redescription set exploration (2018)
  2. Tew, C.; Giraud-Carrier, C.; Tanner, K.; Burton, S.: Behavior-based clustering and analysis of interestingness measures for association rule mining (2014)
  3. Schmidt, Fabian; Spott, Martin: Visualising temporal item sets: guided drill-down with hierarchical attributes (2013) ioport
  4. Boley, Mario; Grosskreutz, Henrik: Approximating the number of frequent sets in dense data (2009) ioport
  5. Lenca, Philippe; Meyer, Patrick; Vaillant, Benoît; Lallich, Stéphane: On selecting interestingness measures for association rules: user oriented description and multiple criteria decision aid (2008)
  6. Ramkumar, Thirunavukkarasu; Srinivasan, Rengaramanujam: Modified algorithms for synthesizing high-frequency rules from different data sources (2008) ioport
  7. Blanchard, Julien; Guillet, Fabrice; Briand, Henri: Interactive visual exploration of association rules with rule-focusing methodology (2007) ioport
  8. Lallich, Stéphane; Vaillant, Benoît; Lenca, Philippe: A probabilistic framework towards the parameterization of association rule interestingness measures (2007)