md`## Visualizations Demonstrated The publication is rich with visualizations as shown below. ### 1. Collection - Screenshots of Researchers' Prototype System UI

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Fig. 2: The dimensionality reduction (DR) view (a) visualizes a result after DR and clustering. The feature contributions view (b) shows the measures of each feature’s contribution to contrasting each cluster with the others. The feature values of the selected cells in (b) are visualized as histograms, as shown in (c). In (d), the user can change the settings for the analysis methods and visualizations.

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Fig. 3: An analysis result of female players from the Tennis Major Tournament Match Statistics dataset.

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Fig. 4: A result of the Nutrient dataset. (a) shows the result after applying t-SNE and DBSCAN. A point’s color and size show the clustering label and the value of ‘calories’, respectively. (b) shows the FCs of each cluster. (c) shows the histograms of the selected cells in (b), as indicated with the colored numbers in both (b) and (c).

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Fig. 5: A screenshot of researchers' prototype system. The dimensionality reduction (DR) view (a) visualizes a result after DR and clustering. The feature contributions view (b) shows the measures of each feature’s contribution to contrasting each cluster with the others. The feature values of the selected cells in (b) are visualized as histograms, as shown in (c). In (d), the user can change the settings for the analysis methods and visualizations.

#### Visualizations The visualizations (UI, rather) are a collection of small visualizations, all illustrating the clustering of different datapoints. These small visualizations include a scatterplot, bar charts and a heatmap. #### Encoding All of the above visualizations are encoded in their traditional sense. Color is used to differenciate different clusters. Color is also used in the heatmaps to represent user-defined value used to perform the classification. #### Data-Ink Ratio The data-ink ratio of the UI is very high. Several visualizations exist and each of them have a lot of data. #### Data Density Much like data-ink ratio, this is high. #### Lie Factor The visualizations don't have much of a lie factor. `