Details
Originalsprache | Englisch |
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Titel des Sammelwerks | Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2016, Proceedings |
Herausgeber/-innen | Björn Bringmann, Elisa Fromont, Nikolaj Tatti, Volker Tresp, Pauli Miettinen, Bettina Berendt, Gemma Garriga |
Herausgeber (Verlag) | Springer Verlag |
Seiten | 8-11 |
Seitenumfang | 4 |
ISBN (Print) | 9783319461304 |
Publikationsstatus | Veröffentlicht - 3 Sept. 2016 |
Veranstaltung | 15th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2016 - Riva del Garda, Italien Dauer: 19 Sept. 2016 → 23 Sept. 2016 |
Publikationsreihe
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Band | 9853 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (elektronisch) | 1611-3349 |
Abstract
We present GMMbuilder, a tool that allows domain scientists to build Gaussian Mixture Models (GMM) that adhere to domain specific constraints like spatial coherence. Domain experts use this tool to generate different models, extract stable object communities across these models, and use these communities to interactively design a final clustering model that explains the data but also considers prior beliefs and expectations of the domain experts.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Theoretische Informatik
- Informatik (insg.)
- Allgemeine Computerwissenschaft
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Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2016, Proceedings. Hrsg. / Björn Bringmann; Elisa Fromont; Nikolaj Tatti; Volker Tresp; Pauli Miettinen; Bettina Berendt; Gemma Garriga. Springer Verlag, 2016. S. 8-11 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 9853 LNCS).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - GMMbuilder-user-driven discovery of clustering structure for bioarchaeology
AU - Mauder, Markus
AU - Bobkova, Yulia
AU - Ntoutsi, Eirini
PY - 2016/9/3
Y1 - 2016/9/3
N2 - We present GMMbuilder, a tool that allows domain scientists to build Gaussian Mixture Models (GMM) that adhere to domain specific constraints like spatial coherence. Domain experts use this tool to generate different models, extract stable object communities across these models, and use these communities to interactively design a final clustering model that explains the data but also considers prior beliefs and expectations of the domain experts.
AB - We present GMMbuilder, a tool that allows domain scientists to build Gaussian Mixture Models (GMM) that adhere to domain specific constraints like spatial coherence. Domain experts use this tool to generate different models, extract stable object communities across these models, and use these communities to interactively design a final clustering model that explains the data but also considers prior beliefs and expectations of the domain experts.
KW - Bioarchaeology
KW - Community detection
KW - Demo
KW - Gaussian mixture models
KW - Interactive clustering
KW - Isotopic mapping
UR - http://www.scopus.com/inward/record.url?scp=84988661501&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-46131-1_2
DO - 10.1007/978-3-319-46131-1_2
M3 - Conference contribution
AN - SCOPUS:84988661501
SN - 9783319461304
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 8
EP - 11
BT - Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2016, Proceedings
A2 - Bringmann, Björn
A2 - Fromont, Elisa
A2 - Tatti, Nikolaj
A2 - Tresp, Volker
A2 - Miettinen, Pauli
A2 - Berendt, Bettina
A2 - Garriga, Gemma
PB - Springer Verlag
T2 - 15th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2016
Y2 - 19 September 2016 through 23 September 2016
ER -