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Beer, Anna; Seidl, Thomas (2019): Graph Ordering and Clustering - A Circular Approach. In: Scientific and Statistical Database Management (Ssdbm 2019): pp. 185-188
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As the ordering of data, particularly of graphs, can influence the result of diverse Data Mining tasks performed on it heavily, we introduce the Circle Index, the first internal quality measurement for orderings of graphs. It is based on a circular arrangement of nodes, but takes in contrast to similar arrangements from the field of, e.g., visual analytics, the edge lengths in this arrangement into account. The minimization of the Circle Index leads to an arrangement which not only offers a simple way to cluster the data using a constrained MinCut in only linear time, but is also visually convincing. We developed the clustering algorithm CirClu, which implements this minimization and MinCut, and compared it with several established clustering algorithms achieving very good results. Simultaneously we compared the Circle Index with several internal quality measures for clusterings. We observed a strong coherence between the Circle Index and the matching of achieved clusterings to the respective ground truths in diverse real world datasets.