Visual Analytics: Combining Automated Discovery with Interactive Visualizations
DS '08 Proceedings of the 11th International Conference on Discovery Science
FpViz: a visualizer for frequent pattern mining
Proceedings of the ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery: Integrating Automated Analysis with Interactive Exploration
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Proceedings of the International Conference on Advanced Visual Interfaces
Visualizations for the spyglass ontology-based information analysis and retrieval system
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Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Seeing beyond reading: a survey on visual text analytics
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Advanced visual analytics methods for literature analysis
LaTeCH '12 Proceedings of the 6th Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities
Visual boosting in pixel-based visualizations
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
ACM SIGMOD Record
Fingerprint matrices: uncovering the dynamics of social networks in prose literature
EuroVis '13 Proceedings of the 15th Eurographics Conference on Visualization
Rule-based visual mappings - with a case study on poetry visualization
EuroVis '13 Proceedings of the 15th Eurographics Conference on Visualization
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In computer-based literary analysis different types of features are used to characterize a text. Usually, only a single feature value or vector is calculated for the whole text. In this paper, we combine automatic literature analysis methods with an effective visualization technique to analyze the behavior of the feature values across the text. For an interactive visual analysis, we calculate a sequence of feature values per text and present them to the user as a characteristic fingerprint. The feature values may be calculated on different hierarchy levels, allowing the analysis to be done on different resolution levels. A case study shows several successful applications of our new method to known literature problems and demonstrates the advantage of our new visual literature fingerprinting.