Readings in information visualization
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
Towards an effective cooperation of the user and the computer for classification
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Interactive machine learning: letting users build classifiers
International Journal of Human-Computer Studies
Graph Visualization and Navigation in Information Visualization: A Survey
IEEE Transactions on Visualization and Computer Graphics
Interactive Construction of Classification Rules
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
VISTA: validating and refining clusters via visualization
Information Visualization
APVis '06 Proceedings of the 2006 Asia-Pacific Symposium on Information Visualisation - Volume 60
Visualizing Graphs - A Generalized View
IV '06 Proceedings of the conference on Information Visualization
IEEE Transactions on Visualization and Computer Graphics
Design and evaluation of visualization support to facilitate decision trees classification
International Journal of Human-Computer Studies
ClusterSculptor: A Visual Analytics Tool for High-Dimensional Data
VAST '07 Proceedings of the 2007 IEEE Symposium on Visual Analytics Science and Technology
Analysis Guided Visual Exploration of Multivariate Data
VAST '07 Proceedings of the 2007 IEEE Symposium on Visual Analytics Science and Technology
Intelligent Visual Analytics Queries
VAST '07 Proceedings of the 2007 IEEE Symposium on Visual Analytics Science and Technology
Towards closing the analysis gap: visual generation of decision supporting schemes from raw data
EuroVis'08 Proceedings of the 10th Joint Eurographics / IEEE - VGTC conference on Visualization
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The analytical derivation of a hypothesis is a process, that requires a transformation of information between raw data and an analytical model. Even though much effort has been spent to support the creation of hypotheses both by algorithmic and visual means, much less research has been done on how the process can be reversed for the verification of existing hypotheses. An evaluation of empirical hypotheses must be grounded in raw data and may require many tedious drill-downs, especially for complex data. We present a concept combining an analytical technique for the representation of hypotheses and their transformation into the data-space. We also show visualization techniques for the formalization of the hypothesis in the analytical space and its visual evaluation in the data space. The evaluation is supported by a visual-matchmaking between original raw data and a modification of this data based upon the assumptions implied by the hypothesis.