A radviz-based visualization for understanding fuzzy clustering results

Fuzzy clustering analysis is an effective method to describe the uncertainty relationship between data objects and clusters. However, fuzzy clustering results will become complex and high-dimensional membership degree matrixes when they contain a large number of data points and multiple clusters. In this paper, we propose a Radviz-based interactive visualization to help users understand fuzzy clustering results. Firstly, we utilize the projection mechanism of Radviz to map the membership degree matrixes onto planar and radial pictures, in which data points with low membership uncertainty are located near Radviz circumference, while the others are scattered in the center of Radviz circle. To provide an informative interactive visualization, we then improve traditional Radviz visualization in many aspects, including implementing an optimal and uneven placement of dimension anchors by using the Prim algorithm, designing visual codings of data points and dimension arcs to express statistical information, combining chord diagram to depict the sharing relationship between clusters, and offering a set of interactions to support deeper exploration. Finally, we use a case study to illustrate the effectiveness and usefulness of our visualization.