Anthropocentric Visualisation of Optimal Cover of Association Rules
نویسندگان
چکیده
Visual mining as an emerging field is gradually witnessing approaches where visual capabilities are of use to mine manageable and useful knowledge. In this respect, the implication of the user at the core of the mining process seems to be one of the key factors in the success of any visual mining system. In this paper, we introduce a new anthropocentric visual mining approach for the extraction and the visualization of a reduced cover of association rules. Given that getting out such a reduced cover is NP-hard problem, we introduce a greedy algorithm that relies on correlation assessment metrics to flag discovered formal concepts as optimal. In addition, we present some snapshots illustrating the key features of the implemented visualization tool that relies on the virtual reality paradigm.
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