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pro vyhledávání: '"Leiber, Collin"'
Finding meaningful groups, i.e., clusters, in high-dimensional data such as images or texts without labeled data at hand is an important challenge in data mining. In recent years, deep clustering methods have achieved remarkable results in these task
Externí odkaz:
http://arxiv.org/abs/2410.09491
Autor:
Beer, Anna, Weber, Pascal, Miklautz, Lukas, Leiber, Collin, Durani, Walid, Böhm, Christian, Plant, Claudia
Detecting arbitrarily shaped clusters in high-dimensional noisy data is challenging for current clustering methods. We introduce SHADE (Structure-preserving High-dimensional Analysis with Density-based Exploration), the first deep clustering algorith
Externí odkaz:
http://arxiv.org/abs/2410.06265
Autor:
Stephan, Andreas, Miklautz, Lukas, Leiber, Collin, de Araujo, Pedro Henrique Luz, Répás, Dominik, Plant, Claudia, Roth, Benjamin
Traditional image clustering techniques only find a single grouping within visual data. In particular, they do not provide a possibility to explicitly define multiple types of clustering. This work explores the potential of large vision-language mode
Externí odkaz:
http://arxiv.org/abs/2406.18589
Publikováno v:
Proceedings of the 2023 SIAM International Conference on Data Mining (SDM) (pp. 109-117). Society for Industrial and Applied Mathematics
Over the last decade, the Dip-test of unimodality has gained increasing interest in the data mining community as it is a parameter-free statistical test that reliably rates the modality in one-dimensional samples. It returns a so called Dip-value and
Externí odkaz:
http://arxiv.org/abs/2312.12050
Publikováno v:
Proceedings of the 2022 SIAM International Conference on Data Mining (SDM) (pp. 226-234). Society for Industrial and Applied Mathematics
High-dimensional datasets often contain multiple meaningful clusterings in different subspaces. For example, objects can be clustered either by color, weight, or size, revealing different interpretations of the given dataset. A variety of approaches
Externí odkaz:
http://arxiv.org/abs/2312.11952
Autor:
Bauer, Lena, Leiber, Collin
This item provides all supplementary material for the paper 'Extension of the Dip-test Repertoire - Efficient and Differentiable p-value Calculation for Clustering' published at the SIAM International Conference on Data Mining (SDM23) April 27 - 29,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0a797ffebe9e1df66bb752a95b389018