Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Anna Beer"'
Autor:
Susanne Arndt, Anna Beer, Ina Blümel, Carsten Elsner, Christian Hauschke, Dagmar Holste, Benjamin Kampe, Micky Lindlar, Gelareh Mofakhamsanie, Andreas Noback, Hedda Saemann, Stephan Tittel, Friedmar Voormann, Katja Wermbter, Roger Winkler
Publikováno v:
Research Ideas and Outcomes, Vol 8, Iss , Pp 1-18 (2022)
University Library Braunschweig (UB Braunschweig), University and State Library Darmstadt (ULB Darmstadt), TIB – Leibniz Information Centre for Technology and Natural Sciences and the Fraunhofer Information Centre for Planning and Building (Fraunho
Externí odkaz:
https://doaj.org/article/4b54218df5544c349cf55644b5699028
Publikováno v:
Proceedings of the VLDB Endowment. 15:3031-3044
Spectral clustering is one of the most advantageous clustering approaches. However, standard Spectral Clustering is sensitive to noisy input data and has a high runtime complexity. Tackling one of these problems often exacerbates the other. As real-w
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4db8fe355b8f3ded3c2dc85f831a8d21
https://doi.org/10.14778/3551793.3551850
https://doi.org/10.14778/3551793.3551850
Publikováno v:
Advances in Data Analysis and Classification. 17:211-238
When researchers publish new cluster algorithms, they usually demonstrate the strengths of their novel approaches by comparing the algorithms’ performance with existing competitors. However, such studies are likely to be optimistically biased towar
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1f5971582013a272ecec74aff4c8aad2
https://doi.org/10.1007/s11634-022-00496-5
https://doi.org/10.1007/s11634-022-00496-5
Publikováno v:
The Semantic Web: ESWC 2022 Satellite Events ISBN: 9783031116087
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::625a292b206cfda46628a77c1dd49a30
https://doi.org/10.1007/978-3-031-11609-4_14
https://doi.org/10.1007/978-3-031-11609-4_14
Publikováno v:
Datenbank-Spektrum. 19:219-230
Chains connecting two or more different clusters are a well known problem of clustering algorithms like DBSCAN or Single Linkage Clustering. Since already a small number of points resulting from, e. g., noise can form such a chain and build a bridge
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7af7ba868e82cc6425a15af0140f5ee9
https://doi.org/10.1007/s13222-019-00324-9
https://doi.org/10.1007/s13222-019-00324-9
Publikováno v:
Beer, A, Stephan, L & Seidl, T 2021, LUCKe-Connecting Clustering and Correlation Clustering . in ICDM (Workshops) . IEEE, pp. 431-440 .
Aarhus University
Aarhus University
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3f942852a37b1fdb9d6cfaefc343e188
https://pure.au.dk/portal/da/publications/lucke--connecting-clustering-and-correlation-clustering(d2060580-45b7-4f53-a768-5571c672de06).html
https://pure.au.dk/portal/da/publications/lucke--connecting-clustering-and-correlation-clustering(d2060580-45b7-4f53-a768-5571c672de06).html
Publikováno v:
ICDM (Workshops)
In this work we propose SRE, the first internal evaluation measure for arbitrary oriented subspace clustering results. For this purpose we present a new perspective on the subspace clustering task: the goal we formalize is to compute a clustering whi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::13fee5853e035150a06a97d7e4674bb8
https://doi.org/10.1109/icdmw51313.2020.00051
https://doi.org/10.1109/icdmw51313.2020.00051
Publikováno v:
Similarity Search and Applications ISBN: 9783030609351
SISAP
SISAP
The amount of data increases steadily, and yet most clustering algorithms perform complex computations for every single data point. Furthermore, Euclidean distance which is used for most of the clustering algorithms is often not the best choice for d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bc402046083b360cdcf07b898ab899f7
https://doi.org/10.1007/978-3-030-60936-8_24
https://doi.org/10.1007/978-3-030-60936-8_24
Autor:
Anna Beer
Margery Kempe. Aemilia Lanyer. Aphra Behn. Lady Mary. Jane Austen. Warned not to write – and certainly not to bite – these women put pen to paper anyway and wrote themselves into history. ‘Smart, funny and highly readable... a tour de force.'A.
Publikováno v:
SSDBM
Beer, A, Kazempour, D, Stephan, L & Seidl, T 2019, ' LUCK-Linear Correlation Clustering Using Cluster Algorithms and a KNN Based Distance Function ', SSDBM '19, pp. 181–-184 . https://doi.org/10.1145/3335783.3335801
Beer, A, Kazempour, D, Stephan, L & Seidl, T 2019, ' LUCK-Linear Correlation Clustering Using Cluster Algorithms and a KNN Based Distance Function ', SSDBM '19, pp. 181–-184 . https://doi.org/10.1145/3335783.3335801
LUCK allows to use any distance-based clustering algorithm to find linear correlated data. For that a novel distance function is introduced, which takes the distribution of the kNN of points into account and corresponds to the probability of two poin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::afc7aea9ff00a65cb1fadfb8616ea2de
https://doi.org/10.1145/3335783.3335801
https://doi.org/10.1145/3335783.3335801