Zobrazeno 1 - 10
of 120
pro vyhledávání: '"Schubert Erich"'
Autor:
Lang, Andreas, Schubert, Erich
Publikováno v:
Int. Conf. on Similarity Search and Applications, 2023
The k-means clustering algorithm is a popular algorithm that partitions data into k clusters. There are many improvements to accelerate the standard algorithm. Most current research employs upper and lower bounds on point-to-cluster distances and the
Externí odkaz:
http://arxiv.org/abs/2410.15117
Autor:
Thordsen, Erik, Schubert, Erich
Many algorithms require discriminative boundaries, such as separating hyperplanes or hyperballs, or are specifically designed to work on spherical data. By applying inversive geometry, we show that the two discriminative boundaries can be used interc
Externí odkaz:
http://arxiv.org/abs/2405.18401
Autor:
Lenssen, Lars, Schubert, Erich
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 measures, which try to provide a general measure to validate clustering res
Externí odkaz:
http://arxiv.org/abs/2309.03751
Autor:
Lenssen, Lars, Schubert, Erich
Partitioning Around Medoids (PAM, k-Medoids) is a popular clustering technique to use with arbitrary distance functions or similarities, where each cluster is represented by its most central object, called the medoid or the discrete median. In operat
Externí odkaz:
http://arxiv.org/abs/2309.02557
Autor:
Schubert, Erich, Lang, Andreas
Hierarchical Agglomerative Clustering (HAC) is likely the earliest and most flexible clustering method, because it can be used with many distances, similarities, and various linkage strategies. It is often used when the number of clusters the data se
Externí odkaz:
http://arxiv.org/abs/2309.02552
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 in the training data tend to become support vectors, and are hence con
Externí odkaz:
http://arxiv.org/abs/2212.13626
Autor:
Schubert, Erich
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 poor conclusions from a common heuristic, the "elbow method". Better alt
Externí odkaz:
http://arxiv.org/abs/2212.12189
Autor:
Lenssen, Lars, Schubert, Erich
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 measures, which try to provide a general measure to validate clustering res
Externí odkaz:
http://arxiv.org/abs/2209.12553
Autor:
Thordsen, Erik, Schubert, Erich
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 (principal component analysis), has been thoroughly researched and the effe
Externí odkaz:
http://arxiv.org/abs/2209.12485
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 answering nearest neighbor and range queries regarding this distance efficie
Externí odkaz:
http://arxiv.org/abs/2111.07761