Information Visualization and Visual Data Mining
IEEE Transactions on Visualization and Computer Graphics
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
The structure of the information visualization design space
INFOVIS '97 Proceedings of the 1997 IEEE Symposium on Information Visualization (InfoVis '97)
A User-driven and Quality-oriented Visualization for Mining Association Rules
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Quality Measures in Data Mining (Studies in Computational Intelligence)
Quality Measures in Data Mining (Studies in Computational Intelligence)
FromDaDy: Spreading Aircraft Trajectories Across Views to Support Iterative Queries
IEEE Transactions on Visualization and Computer Graphics
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Data Mining aims at extracting maximum of knowledge from huge databases. It is realized by an automatic process or by data visual exploration with interactive tools. Automatic data mining extracts all the patterns which match a set of metrics. The limit of such algorithms is the amount of extracted data which can be larger than the initial data volume. In this article, we focus on association rules extraction with Apriori algorithm. After the description of a characterization model of a set of association rules, we propose to explore the results of a Data Mining algorithm with an interactive visual tool. There are two advantages. First it will visualize the results of the algorithms from different points of view (metrics, rules attributes...). Then it allows us to select easily inside large set of rules the most relevant ones.