Multivariate visualization using metric scaling
VIS '97 Proceedings of the 8th conference on Visualization '97
Visualizing the non-visual: spatial analysis and interaction with information from text documents
INFOVIS '95 Proceedings of the 1995 IEEE Symposium on Information Visualization
Vector fields simplification — a case study of visualizing climate modeling and simulation data sets
Proceedings of the conference on Visualization '00
Multivariate visualization with data fusion
Information Visualization
Information Processing and Management: an International Journal
Graph Signatures for Visual Analytics
IEEE Transactions on Visualization and Computer Graphics
Unsupervised clustering of multidimensional distributions using earth mover distance
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Analyzing the composition of cities using spatial clustering
Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing
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Today, as data sets used in computations grow in size and complexity, the technologies developed over the years to deal with scientific data sets have become less efficient and effective. Many frequently used operations, such as eigenvector computation, could quickly exhaust our desktop workstations once the data size reaches certain limits. On the other hand, the high-dimensional data sets we collect every day don't relieve the problem. Many conventional metric designs that build on quantitative or categorical data sets cannot be applied directly to heterogeneous data sets with multiple data types. While building new machines with more resources might conquer the data size problems, the complexity of today's computations requires a new breed of projection techniques to support analysis of the data and verification of the results. We introduce the concept of a data signature, which captures the essence of a scientific data set in a compact format, and use it to conduct analysis as if using the original. A time-dependent climate simulation data set demonstrates our approach and presents the results