Technometrics
Guidelines for using multiple views in information visualization
AVI '00 Proceedings of the working conference on Advanced visual interfaces
Multidimensional information visualization through sliding rods
AVI '00 Proceedings of the working conference on Advanced visual interfaces
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
A General Method for Scaling Up Machine Learning Algorithms and its Application to Clustering
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
High Dimensional Brushing for Interactive Exploration of Multivariate Data
VIS '95 Proceedings of the 6th conference on Visualization '95
Angular Brushing of Extended Parallel Coordinates
INFOVIS '02 Proceedings of the IEEE Symposium on Information Visualization (InfoVis'02)
Interactive visual summarization of multidimensional data
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Wakame: sense making of multi-dimensional spatial-temporal data
Proceedings of the International Conference on Advanced Visual Interfaces
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Summarizing large multidimensional datasets is a challenging task, often requiring extensive investigation by a user to identify overall trends and important exceptions to them. While many visualization tools help a user produce a single summary of the data at a time, they require the user to explore the dataset manually. Our idea is to have the computer perform an exhaustive search and inform the user about where further investigation is warranted. Our algorithm takes a large, multidimensional dataset as input, along with a specification of the user's goals, and produces a concise summary that can be clearly visualized in bar graphs or linegraphs. We demonstrate our techniques in a sample prototype for summarizing information stored in spreadsheet databases.