Data and Knowledge Visualization in Knowledge Discovery Process
VISUAL '02 Proceedings of the 5th International Conference on Recent Advances in Visual Information Systems
Visualization support for a user-centered KDD process
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Multiple foci visualisation of large hierarchies with FlexTree
Information Visualization
Knowledge in digital decision support system
UAHCI'11 Proceedings of the 6th international conference on Universal access in human-computer interaction: applications and services - Volume Part IV
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Abstract: Decision tree induction is certainly among the most applicable learning techniques due to its power and simplicity. However learning decision trees from large datasets, particularly in data mining, is quite different from learning from small or moderately sized datasets. When learning from large datasets, decision tree induction programs often produce very large trees. How to efficiently visualize trees in the learning process, particularly large trees, is still questionable and currently requires efficient tools. The paper presents a visualization tool for interactive learning of large decision trees, that includes a new visualization technique called T2.5D (Trees 2.5 Dimensions). After a brief discussion on requirements for tree visualizers and related work, the paper focuses on presenting developing techniques for two issues: (1) how to visualize efficiently large decision trees; and (2) how to visualize decision trees in the learning process.