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NovoSpark Visualizer 1.2

NovoSpark Visualizer is an advanced, powerful and feature rich visualization program that enables qualitative analysis of multi-dimensional data on a graphical image. The product comes with a variety of image viewing options that helps you get the most representative view of your data. With NovoSpark Visualizer you will be able to visually compare individual observations and entire datasets, find data anomalies, identify data clusters and do many other things you used to do in your day-to-day data analysis, but much quicker and more efficiently.

Visualizer uses unique data visualization method to represent each multidimensional record as a colored curve, so the entire dataset or multiple datasets of any dimensionality can be visualized on a single image, with the same-colored curves representing records from the same dataset. The little ticks on the white-colored axis mark location of individual curves, while red triangular markers immediately identify the curves that represent "abnormal" records and may require special attention of a researcher.

Visualizer renders multi-dimensional data as a graphical image allowing you to focus on its visual characteristics rather than the tables of raw data. Our visualization technology guarantees that the resulting image preserves all information about the original data, so you can safely rely on the results inferred from the visual analysis of your data samples. The application supports all standard operations with images including rotation, moving, stretching, as well as using various image viewing options like projection views and "noise" filters.

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• NovoSpark Visualizer is an advanced, powerful and feature rich visualization program that enables qualitative analysis of multi-dimensional data on a graphical image. The product comes with a variety of image viewing options that helps you get the most representative view of your data. With NovoSpark Visualizer you will be able to visually compare individual observations and entire datasets, find data anomalies, and identify data clusters.
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