Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries
A visual analysis tool that smoothly switches between tabular forms and parallel coordinates
Proceedings of the 2011 Visual Information Communication - International Symposium
Dual analysis of DNA microarrays
Proceedings of the 12th International Conference on Knowledge Management and Knowledge Technologies
Interactive visual analysis of temporal cluster structures
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
Comparison of multiple weighted hierarchies: visual analytics for microbe community profiling
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
Integrating cluster formation and cluster evaluation in interactive visual analysis
Proceedings of the 27th Spring Conference on Computer Graphics
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When analyzing multidimensional, quantitative data, the comparison of two or more groups of dimensions is a commontask. Typical sources of such data are experiments in biology, physics or engineering, which are conducted in different configurationsand use replicates to ensure statistically significant results. One common way to analyze this data is to filter it using statistical methodsand then run clustering algorithms to group similar values. The clustering results can be visualized using heat maps, which showdifferences between groups as changes in color. However, in cases where groups of dimensions have an a priori meaning, it is notdesirable to cluster all dimensions combined, since a clustering algorithm can fragment continuous blocks of records. Furthermore,identifying relevant elements in heat maps becomes more difficult as the number of dimensions increases. To aid in such situations,we have developed Matchmaker, a visualization technique that allows researchers to arbitrarily arrange and compare multiple groupsof dimensions at the same time. We create separate groups of dimensions which can be clustered individually, and place them in anarrangement of heat maps reminiscent of parallel coordinates. To identify relations, we render bundled curves and ribbons betweenrelated records in different groups. We then allow interactive drill-downs using enlarged detail views of the data, which enable in-depthcomparisons of clusters between groups. To reduce visual clutter, we minimize crossings between the views. This paper concludeswith two case studies. The first demonstrates the value of our technique for the comparison of clustering algorithms. In the second,biologists use our system to investigate why certain strains of mice develop liver disease while others remain healthy, informallyshowing the efficacy of our system when analyzing multidimensional data containing distinct groups of dimensions.