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Anja Susanne Camara:

Animierte Transitionen für die Visualisierung von Veränderungen in Clustering-Ergebnissen

Bachelor of Science (B.Sc.)


Clustering is a commonly used method to group multidimensional data based on their similarities. The result of the clustering process depends on the parameters used by the clustering algorithm. If the parameters change, a new clustering emerges that may not have any similarity to the previous clustering. The purpose of animated transitions is to make it easier for an observer to comprehend how two such clusterings relate to each other and especially which changes were necessary to transform the first clustering into the second one. In this Bachelor thesis, I first summarize the most relevant insights into animated transitions. Based on these insights, I then design and implement animated transitions for clusters with convex hulls. Specifically, the concepts of cluster operations such as adding new clusters, deleting clusters that are no longer needed, as well as cluster splitting and merging will be used. In a pilot study, comprehension of the different cluster operations and also the advan- tages and disadvantages of animated transitions versus simple fade-in/fade-out are evaluated. The pilot study should also help determine the preferred transition duration used by the animations and evaluate whether the participants consider the visualization of the clusters by using a convex hull to be helpful. The findings of the pilot study are as follows: The participants were, in most cases, able to correctly recognize the cluster operations. They considered the convex hull to be necessary to better comprehend the cluster visualizations. An average transition duration between 1.5s and 3s was favored. All participants preferred animated transitions to simple fade-in/fade-out between clusterings. One reason was that they could better comprehend which changes took place and how they occured. The participants also felt that the animated transitions looked „more organic“ and „pleasing“. These insights are used for the final implementation.