@ -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: 146) 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: 147) 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).
@ -40,9 +40,9 @@ The following instructions describe how to reach the results present in Figure 1
**Tip:** The *Reset Filters* button illustrated in Figure 1(h), resets all the applied interactions in case you made a mistake and you want to redraw something.
- Step 1: We press the *Execute new t-SNE analysis* button that is observable in Figure 1(a).
- Step 2: From the new pop-up tab named as *t-SNE Grid Search Resulting Diverse Projections [Sorting Projections According to Metric:* we select*Continuity (C)* quality metric.
- Step 3: From the five projections seen on the top (out of the 25), we choose the fourth projection with *Quality Metrics Average (QMA)* approximately 0.43 and rather high *Continuity (C)* (around 0.69). Next, we press the *Confirm* the projection and wait for the system to load all the views. The t-SNE overview is the same as in Figure 7(a) and the main projection can be seen in Figure 7(a). Also, Figures 7(b) and 7(d) show the exact same representations as you should have in front of you.
- Step 1: You should press the *Execute new t-SNE analysis* button that is observable in Figure 1(a).
- Step 2: From the new pop-up tab named as *t-SNE Grid Search Resulting Diverse Projections [Sorting Projections According to Metric*, you have to select the*Continuity (C)* quality metric.
- Step 3: From the five projections seen on the top (out of the 25), you choose the fourth projection with *Quality Metrics Average (QMA)* approximately 0.43 and rather high *Continuity (C)* (around 0.69). Next, you press the *Confirm* the projection and wait for the system to load all the views. The t-SNE overview is the same as in Figure 7(a) and the main projection can be seen in Figure 7(a). Also, Figures 7(b) and 7(d) show the exact same representations as you should have in front of you.
- Step 4: To reproduce Figure 7(e), you have to use the *Group Selection* option from the *Interaction Modes* illustrated in Figure 1(h). Afterward, you should carefully select the areas depicted in red color in Figure 7(c). To select those areas, you hold the left mouse and draw a shape with the lasso interaction provided by the tool. The first area is "High remaining cost", the second is "Low-density ”tip”", and the third "Low-density cluster". Please note that the results in Figure 7(e) start from 20 to 40 k-values of nearest neighbors.
- 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.
