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ACM Transactions on Graphics (TOG)
Visual classification: an interactive approach to decision tree construction
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
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Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
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International Journal of Human-Computer Studies
Proceedings of the 8th international conference on Intelligent user interfaces
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Information Visualization
PaintingClass: interactive construction, visualization and exploration of decision trees
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
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INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
Knowledge Precepts for Design and Evaluation of Information Visualizations
IEEE Transactions on Visualization and Computer Graphics
Vizster: Visualizing Online Social Networks
INFOVIS '05 Proceedings of the Proceedings of the 2005 IEEE Symposium on Information Visualization
Revealing Structure within Clustered Parallel Coordinates Displays
INFOVIS '05 Proceedings of the Proceedings of the 2005 IEEE Symposium on Information Visualization
Give chance a chance: modeling density to enhance scatter plot quality through random data sampling
Information Visualization
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IEEE Transactions on Visualization and Computer Graphics
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IEEE Transactions on Visualization and Computer Graphics
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ACM SIGKDD Explorations Newsletter
Louhi '10 Proceedings of the NAACL HLT 2010 Second Louhi Workshop on Text and Data Mining of Health Documents
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Proceedings of the 12th International Conference on Knowledge Management and Knowledge Technologies
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The aim of this work is to survey and reflect on the various ways to integrate visualization and data mining techniques toward a mixed-initiative knowledge discovery taking the best of human and machine capabilities. Following a bottom-up bibliographic research approach, the article categorizes the observed techniques in classes, highlighting current trends, gaps, and potential future directions for research. In particular it looks at strengths and weaknesses of information visualization and data mining, and for which purposes researchers in infovis use data mining techniques and reversely how researchers in data mining employ infovis techniques. The article further uses this information to analyze the discovery process by comparing the analysis steps from the perspective of information visualization and data mining. The comparison permits to bring to light new perspectives on how mining and visualization can best employ human and machine skills.