Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Ines Färber"'
Publikováno v:
Knowledge and Information Systems. 40:243-278
In this work, we propose a new method to find homogeneous object groups in a single vertex-labeled graph. The basic premise is that many prevalent datasets consist of multiple types of information: graph data to represent the relations between object
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::27dff2bcc0df8e625e7154caa8b6006a
https://doi.org/10.1007/s10115-013-0640-z
https://doi.org/10.1007/s10115-013-0640-z
Autor:
Ines Färber
Modern storage systems allow to capture data in its full complexity. As implication for the data mining task of clustering, multiple, alternative, and valid clusterings can be identified for a single dataset. A second observation is that clustering b
Autor:
Christian Böhm, Johannes Merkle, Annahita Oswald, Thomas Seidl, Christoph Busch, Xuebing Zhou, Peter Wackersreuther, Alexander Opel, Bianca Wackersreuther, Sebastian Abt, Ines Färber, Ulrike Korte, Sergej Fries, Alexander Nouak, Claudia Nickel
Publikováno v:
Datenschutz und Datensicherheit - DuD. 35:183-191
Biometrische Systeme sind zwar technisch weit ausgereift und bieten heute Erkennungsleistungen, die noch vor 10 Jahren unerreichbar waren. Jedoch ist ein weit verbreiteter Einsatz von biometrischen Authentisierungsverfahren durch Bedenken hinsichtlic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fbed28f83091fa2521c07cc8377e9162
https://doi.org/10.1007/s11623-011-0047-5
https://doi.org/10.1007/s11623-011-0047-5
Publikováno v:
Proceedings of the VLDB Endowment. 3:1633-1636
Large data resources are ubiquitous in science and business. For these domains, an intuitive view on the data is essential to fully exploit the hidden knowledge. Often, these data can be semantically structured by concepts. Since the determination of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::22730f3449b33527eecbd0b186534bae
https://doi.org/10.14778/1920841.1921058
https://doi.org/10.14778/1920841.1921058
Autor:
Michael Sedlmair, Michael Behrisch, Michael Hund, Tobias Schreck, Daniel A. Keim, Thomas Seidl, Ines Färber
Publikováno v:
Similarity Search and Applications ISBN: 9783319250861
SISAP
SISAP
Computing the similarity between objects is a central task for many applications in the field of information retrieval and data mining. For finding k-nearest neighbors, typically a ranking is computed based on a predetermined set of data dimensions a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::af547451d5f89cb7e18523886a60f5fa
https://doi.org/10.1007/978-3-319-25087-8_29
https://doi.org/10.1007/978-3-319-25087-8_29
Publikováno v:
KDD
Since data is often multi-faceted in its very nature, it might not adequately be summarized by just a single clustering. To better capture the data's complexity, methods aiming at the detection of multiple, alternative clusterings have been proposed.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c176d42bb17123d5ec8b82a508acd710
https://doi.org/10.1145/2623330.2623734
https://doi.org/10.1145/2623330.2623734
Publikováno v:
Towards the Internet of Services: The THESEUS Research Program ISBN: 9783319067544
Towards the Internet of Services
Towards the Internet of Services
The “Machining Intelligence Network” (MachInNet) project tackles the challenges to “unearthing” manufacturing knowledge from NC codes (numerical control codes), tool layouts and other manufacturing documents, and to making it accessible for d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cee6ca92935fc91a256facbd816d84ee
https://doi.org/10.1007/978-3-319-06755-1_32
https://doi.org/10.1007/978-3-319-06755-1_32
Publikováno v:
ICDM
Clustering graphs annotated with feature vectors has recently gained much attention. The goal is to detect groups of vertices that are densely connected in the graph as well as similar with respect to their feature values. While early approaches trea
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8d8795a3e7d5dab409de05e38090153f
https://doi.org/10.1109/icdm.2013.110
https://doi.org/10.1109/icdm.2013.110
Publikováno v:
Advances in Knowledge Discovery and Data Mining ISBN: 9783642374524
PAKDD (1)
PAKDD (1)
Large graphs are ubiquitous in today’s applications. Besides the mere graph structure, data sources usually provide information about single objects by feature vectors. To realize the full potential for knowledge extraction, recent approaches consi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2ef17ab6560f77fe43a7cdd06c049660
https://doi.org/10.1007/978-3-642-37453-1_22
https://doi.org/10.1007/978-3-642-37453-1_22
Autor:
Fabian Maas, Andrada Tatu, Daniel A. Keim, Tobias Schreck, Thomas Seidl, Enrico Bertini, Ines Färber
Publikováno v:
IEEE VAST
In explorative data analysis, the data under consideration often resides in a high-dimensional (HD) data space. Currently many methods are available to analyze this type of data. So far, proposed automatic approaches include dimensionality reduction
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3475035a188ad3b8ffa313cf00c9d0c3
https://doi.org/10.1109/vast.2012.6400488
https://doi.org/10.1109/vast.2012.6400488