Time-Varying Data Visualization Using Clustered Heatmap and Dual Scatterplots

Heatmap is one of the effective representations for time-varying data visualization. It may require large display spaces when an input dataset contains large number of data items or time steps. We may often want mechanisms to interactively filter non-important data items or time steps, so that we can form appropriate sizes of heatmaps and focus on important data items or time steps. This paper presents a heatmap-based time-varying data visualization technique featuring an interactive mechanism to display meaningful data items and time steps. This technique firstly calculates distances between arbitrary pairs of data items, and constructs a dendrogram consisting the data items. It then generates clusters of the data items and displays the data items belonging to the specified sizes of clusters in the heatmap, so that we can focus on groups of similar or correlated data items. It applies a similar mechanism to a set of time steps so that we can remove outlier time steps from the heatmap. Our implementation features two scatterplots, which represent distribution of data items and time steps respectively, and slider widgets to interactively adjust the thresholds of the clustering process. We can intuitively understand how clusters of data items or time steps are constructed, by looking at the scatterplots while operating the sliders.