Classification and visualization for high-dimensional data
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Visualizing multi-dimensional clusters, trends, and outliers using star coordinates
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Inventing Discovery Tools: Combining Information Visualization with Data Mining
DS '01 Proceedings of the 4th International Conference on Discovery Science
Dynamic query tools for time series data sets: timebox widgets for interactive exploration
Information Visualization
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
IEEE Transactions on Visualization and Computer Graphics
Integrating Data Clustering and Visualization for the Analysis of 3D Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Enhanced visual separation of clusters by M-mapping to facilitate cluster analysis
VISUAL'07 Proceedings of the 9th international conference on Advances in visual information systems
Tracing Tuples Across Dimensions: A Comparison of Scatterplots and Parallel Coordinate Plots
Computer Graphics Forum
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Users can better understand complex data sets by combining insights from multiple coordinated visual displays that include relevant domain knowledge. When dealing with multidimensional data and clustering results, the most familiar displays and comprehensible are 1- and 2-dimensional projections (histograms, and scatterplots). Other easily understood displays of domain knowledge are tabular and hierarchical information for the same or related data sets. The novel parallel coordinates view [6] powered by a direct-manipulation search, offers strong advantages, but requires some training for most users. We provide a review of related work in the area of information visualization, and introduce new tools and interaction examples on how to incorporate users' domain knowledge for understanding clustering results. Our examples present hierarchical clustering of gene expression data, coordinated with a parallel coordinates view and with the gene annotation and gene ontology.