243 citations · 694 across the 14 of their papers we have counts for
15 papers
LOSDD: Leave-Out Support Vector Data Description for Outlier Detection
Daniel Boiar, Thomas Liebig, Erich Schubert
Support Vector Machines have been successfully used for one-class classification (OCSVM, SVDD) when trained on clean data, but they work much worse on dirty data: outliers present…
Stop using the elbow criterion for k-means and how to choose the number of clusters instead
Erich Schubert
A major challenge when using k-means clustering often is how to choose the parameter k, the number of clusters. In this letter, we want to point out that it is very easy to draw po…
Clustering by Direct Optimization of the Medoid Silhouette
Lars Lenssen, Erich Schubert
The evaluation of clustering results is difficult, highly dependent on the evaluated data set and the perspective of the beholder. There are many different clustering quality measu…
On Projections to Linear Subspaces
Erik Thordsen, Erich Schubert
The merit of projecting data onto linear subspaces is well known from, e.g., dimension reduction. One key aspect of subspace projections, the maximum preservation of variance (prin…
EmbAssi: Embedding Assignment Costs for Similarity Search in Large Graph Databases
Franka Bause, Erich Schubert, Nils M. Kriege
The graph edit distance is an intuitive measure to quantify the dissimilarity of graphs, but its computation is NP-hard and challenging in practice. We introduce methods for answer…
Metric Indexing for Graph Similarity Search
Franka Bause, David B. Blumenthal, Erich Schubert +1
Finding the graphs that are most similar to a query graph in a large database is a common task with various applications. A widely-used similarity measure is the graph edit distanc…