4D Heatmap Demo

In order to demonstrate HeatMiner 4D Heatmaps in practice, we downloaded a data set containing human body measurements from this site and opened it with Microsoft Excel. The data set contains 24 body measures for 247 male and 260 female persons and we wanted to visualize how human weight depends on age, height and waist girth.

First, we separated male and female rows to different worksheets simply by copying the whole table and deleting unnecessary rows from the copy (gender=1 for males and 0 for females). Then we selected the interesting rows and copied them into new worksheet so that the weight is the first column to determine heatmap colors as specified in the input specification of the HeatMiner 4d-heatmap solution. The pictures below show snapshots of the original 25-column worksheet and the copy with only 4 columns.

original-columns selected-columns

Finally, we saved the worksheet with the selected columns as a tabulator separated text files (as required in the input file format specification) by clicking 'save as' from the file menu and then selecting 'save as type' option 'Text (tab delimited) (*.txt)' from the list. This file can now be sent to HeatMiner algorithm via Cloud'N'Sci.fi.

As a result you get a HeatMiner heatmap. The snapshot image right below shows the visualization generated from the female measurements and the next image is the corresponding heatmap for males. Click the link below the image to view a 3D applet which can be rotated by mouse. Please note that the applet requires Java 3D support from your browser and downloads several megabytes of data.

4d heatmap of female weights

Click to view as a Java 3D applet

4d heatmap of male weights

Click to view as a Java 3D applet

The default HeatMiner settings work well for most data sets, but in some cases visualization granularity or smoothing parameters should be adjusted for optimal results. Also legend colors and thresholds can be customized. Please contact Agience Oy Ltd if you wish to use custom parameters optimized for your data.

See also

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