@ -3,7 +3,7 @@ This Git repository contains the code that accompanies a research publication so
**Note:** t-viSNE is optimized to work better for the 2560x1440 resolution (1440p/QHD (Quad High Definition)). Any other resolution might need manual adjustment of your browser's zoom level to work properly.
**Note:** This repository contains a frozen version (commit id: 148) that matches the paper's implementation. However, we plan to further improve the implementation in the future.
**Note:** This repository contains a frozen version (commit id: 149) that matches the paper's implementation. However, we plan to further improve the implementation in the future.
# Data Sets #
The data sets are available online from the [UCI Machine Learning Repository](http://archive.ics.uci.edu/ml/index.php). We use the Iris data set, Breast Cancer Wisconsin (Original) data set, Pima Indians Diabetes data set, and the SPECTF Heart data set. All data sets are transformed in comma separated format (csv).
@ -47,7 +47,7 @@ The following instructions describe how to reach the results present in Figure 1
- Step 5: For Figure 7(g), you use the *Dimension Correlation* option from the *Interaction Modes* illustrated in Figure 1(h). Then you use left click to draw a point and once more another left click for another point until you have drawn precisely the line seen in Figure 7(g) and Figure 7(c) in blue. Afterward, with the right mouse click you confirm the drawn shape and observe in the *Dimension Correlation* view depicted in Figure 1(j) the values of Figure 7(f). Finally, if you click the "Insulin" dimension you get Figure 7(g). The exact values of "Insulin" are now shown in the legend of Figure 1(i) instead of the *Density* and the *Remaining Cost* as it was before in Figure 7(c, bottom-right corner).
- Step 6: The next step is to choose the *Quality Metrics Average (QMA)* option instead of *Continuity (C)* with the dropdown selection seen in Figure 1(e). This will sort the projections based on the average value of all the provided quality metrics.
- Step 7: To receive the image shown in Figure 1, you choose with the *Group Selection* described in Step 4 the third cluster named as "Low-density cluster" (cf. Figure 7(c)). Then you should click the *Optimize Selection* button (see Figure 1(e), which is going to resort all the projections. Afterward, you click and choose the first projection out of the six shown in Figure 1(e). Now Figure 1 is loaded for you.
- Step 7: Finally, you use the *Dimension Correlation* option from the *Interaction Modes* illustrated in Figure 1(h) and draw a line (i.e., click to place two individual points) as shown in Figure 1(f). Next, press right click to confirm the drawn shape as in Step 5. If the line is drawn correctly, then you just go to the *Visual Mapping* panel (cf. Figure 1(i)) and you select *KNN* instead of *Distance* for the *Correlation measurement*. After this modification, you will find the *K-value (KNN)* field which should be set to 34 (see Figure 1(i)).
- Step 8: Finally, you use the *Dimension Correlation* option from the *Interaction Modes* illustrated in Figure 1(h) and draw a line (i.e., click to place two individual points) as shown in Figure 1(f). Next, press right click to confirm the drawn shape as in Step 5. If the line is drawn correctly, then you just go to the *Visual Mapping* panel (cf. Figure 1(i)) and you select *KNN* instead of *Distance* for the *Correlation measurement*. After this modification, you will find the *K-value (KNN)* field which should be set to 34 (see Figure 1(i)).
**Outcome:** The above process describes how you will be able to reproduce precisely the results presented in Figures 1 and 7 of the paper. Thank you for your time!
