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pro vyhledávání: '"Hannen, Matthias"'
Analyzing high dimensional data is a challenging task. For these data it is known that traditional clustering algorithms fail to detect meaningful patterns. As a solution, subspace clustering techniques have been introduced. They analyze arbitrary su
Externí odkaz:
http://arxiv.org/abs/1407.3850
Publikováno v:
E3S Web of Conferences, Vol 111, p 05002 (2019)
Data from building automation systems is so far used for the operation of building systems and components only. The following work shows how this data can be used to enhance the building’s performance by strategically detecting potential sources fo
Externí odkaz:
https://doaj.org/article/108e48c9a2464ce9bb8d38db59cd1403
Autor:
Auer, Thomas, Lauss, Lukas, Heissler, Karl Martin, Maderspacher, Johannes, Reiß, Dirk, Mehnert, Jan, Rumpe, Bernhard, Stüber, Sebastian, Hannen, Matthias, Plesser, Stefan, Pinkernell, Claas, Kröker, Alex, Gentemann, Roland
Das Ziel dieses Forschungsvorhabens ist die Entwicklung von skalierbaren und automatisierten Lösungen zur Minimierung von Performance Gaps, die durch diverse Mängel während der Planung, Errichtung und dem Betrieb von Gebäuden entstehen können. D
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::205451f95deee83645913f2929757815
Analyzing high dimensional data is a challenging task. For these data it is known that traditional clustering algorithms fail to detect meaningful patterns. As a solution, subspace clustering techniques have been introduced. They analyze arbitrary su
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c502a76621022294574bc97ceb0ea639
http://arxiv.org/abs/1407.3850
http://arxiv.org/abs/1407.3850
Autor:
Müller, Emmanuel, Schiffer, Matthias, Gerwert, Patrick, Hannen, Matthias, Jansen, Timm, Seidl, Thomas
Publikováno v:
Machine Learning & Knowledge Discovery in Databases (9783642159381); 2010, p607-610, 4p