Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Design and evaluation of visualization support to facilitate decision trees classification
International Journal of Human-Computer Studies
Visual Analytics: A 2D-3D visualization support for human-centered rule mining
Computers and Graphics
The Projection Explorer: A Flexible Tool for Projection-based Multidimensional Visualization
SIBGRAPI '07 Proceedings of the XX Brazilian Symposium on Computer Graphics and Image Processing
ICMLA '07 Proceedings of the Sixth International Conference on Machine Learning and Applications
Visualizing frequent itemsets, association rules, and sequential patterns in parallel coordinates
ICCSA'03 Proceedings of the 2003 international conference on Computational science and its applications: PartI
A framework for visualizing association mining results
ISCIS'06 Proceedings of the 21st international conference on Computer and Information Sciences
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We introduce a user-driven approach to mining association rules, integrated into a visualization system, called I2E, that allows miners to depart from a reduced and representative subset of rules to interactively explore the whole rule space. A visualization of the space of k-itemsets displayed after each iteration of Apriori allows the miner to guide rule extraction by exploring the space of itemsets. Miners can discard itemsets considered not relevant and define clusters of related itemsets to perform rule filtering, so that uninteresting rules are removed while preserving itemset coverage in the resulting rule set. This reduced set provides a starting point to explore the rule space with an interface that supports pairwise comparisons between rules, according to some defined criteria. We describe the results obtained from applying the proposed approach and its supporting system on a case study with a real dataset containing information on cattle commercialized in Brazil.