---
_id: '12274'
abstract:
- lang: eng
text: The Toothed Orchid, Neotinea tridentata, has been elected for Orchid of
the Year 2019 by the steering committees of the German Native Orchids Associations (Arbeitskreise
Heimische Orchideen Deutschlands, AHO) Etymology, characteristics and life cycle
as well as ecology and habitat of the species are specified. The range of the
species is described and a distribution map is presented for Germany. Threats
are discussed and methods of conservation management of the species are pointed
out. Finally possible consequences of climate change for the species are discussed
- lang: fre
text: L’orchis tridenté, Neotinea tridentata, a été élue orchidée de l‘année 2019
par les comités directeurs des associations allemandes d‘orchidées indigènes (Arbeitskreise
Heimische Orchideen Deutschlands, AHO) L‘étymologie, les caractéristiques et
le cycle de vie ainsi que l‘écologie et l‘habitat de cette espèce sont spécifiés.
L‘aire de répartition du taxon est décrite et une carte de répartition est présentée
pour l‘Allemagne. Les menaces sont discutées et les méthodes de gestion de la
conservation de l‘espèce sont soulignées. Enfin, les consé quences possibles du changement climatique
pour l‘espèce sont abordées.
author:
- first_name: Mathias
full_name: Lohr, Mathias
id: '22021'
last_name: Lohr
- first_name: Jutta
full_name: Haas, Jutta
last_name: Haas
citation:
ama: Lohr M, Haas J. L’orchidée tridentée, Neotinea tridentata. . L’Orchidophile.
2024;241:139-148.
apa: Lohr, M., & Haas, J. (2024). L’orchidée tridentée, Neotinea tridentata.
. L’Orchidophile, 241, 139–148.
bjps: Lohr M and Haas J (2024) L’orchidée Tridentée, Neotinea Tridentata.
. L’Orchidophile 241, 139–148.
chicago: 'Lohr, Mathias, and Jutta Haas. “L’orchidée Tridentée, Neotinea Tridentata.
.” L’Orchidophile 241 (2024): 139–48.'
chicago-de: 'Lohr, Mathias und Jutta Haas. 2024. L’orchidée tridentée, Neotinea
tridentata. . L’Orchidophile 241: 139–148.'
din1505-2-1: 'Lohr, Mathias ; Haas, Jutta: L’orchidée tridentée, Neotinea
tridentata. . In: L’Orchidophile Bd. 241, FFO (2024), S. 139–148'
havard: M. Lohr, J. Haas, L’orchidée tridentée, Neotinea tridentata. , L’Orchidophile.
241 (2024) 139–148.
ieee: M. Lohr and J. Haas, “L’orchidée tridentée, Neotinea tridentata. ,” L’Orchidophile,
vol. 241, pp. 139–148, 2024.
mla: Lohr, Mathias, and Jutta Haas. “L’orchidée Tridentée, Neotinea Tridentata.
.” L’Orchidophile, vol. 241, 2024, pp. 139–48.
short: M. Lohr, J. Haas, L’Orchidophile 241 (2024) 139–148.
ufg: 'Lohr, Mathias/Haas, Jutta: L’orchidée tridentée, Neotinea tridentata.
, in: L’Orchidophile 241 (2024), S. 139–148.'
van: Lohr M, Haas J. L’orchidée tridentée, Neotinea tridentata. . L’Orchidophile.
2024;241:139–48.
date_created: 2025-01-02T17:11:53Z
date_updated: 2025-01-07T07:47:04Z
ddc:
- '570'
department:
- _id: DEP1310
has_accepted_license: '1'
intvolume: ' 241'
keyword:
- Orchidaceae
- Neotinea tridentata
- orchidée de l’année 2019
- caractéristiques
- distribution
- cycle de vie
- gestion
- menaces
- conservation
- statut de protection
language:
- iso: eng
page: 139-148
publication: L’Orchidophile
publication_identifier:
issn:
- 2498-3713
- 0750-0386
publication_status: published
publisher: FFO
related_material:
link:
- relation: confirmation
url: https://www.researchgate.net/publication/387485714_L'orchidee_tridentee_Neotinea_tridentata
status: public
title: 'L’orchidée tridentée, Neotinea tridentata. '
type: industry_journal_article
user_id: '83781'
volume: 241
year: '2024'
...
---
_id: '10590'
abstract:
- lang: ger
text: Das Kriechende Netzblatt, Goodyera repens, das von den Vorständen der Arbeitskreise
Heimische Orchideen Deutschlands (AHOs) zur Orchidee des Jahres 2021 gewählt wurde,
wird vorgestellt. Namensgebung, morphologische Merkmale und Lebenszyklus sowie
Ökologie und Lebensräume werden charakterisiert. Das Areal der Art wird beschrieben
und die Verbreitung für Deutschland kartographisch dargestellt. Gefährdungsursachen
werden erörtert und Schutzmaßnahmen aufgezeigt. Abschließend werden signifikante
Konsequenzen des Klimawandels für die Art diskutiert und in Beispielen dargelegt.
author:
- first_name: Marco
full_name: Klüber, Marco
last_name: Klüber
- first_name: Mathias
full_name: Lohr, Mathias
id: '22021'
last_name: Lohr
citation:
ama: 'Klüber M, Lohr M. Das Kriechende Netzblatt Goodyera repens (L.) R.Br. – Orchidee
des Jahres 2021 . Journal europäischer Orchideen : Mitteilungsblatt des AHO
Baden-Württemberg . 2022;54(1-2):3-27.'
apa: 'Klüber, M., & Lohr, M. (2022). Das Kriechende Netzblatt Goodyera repens
(L.) R.Br. – Orchidee des Jahres 2021 . Journal europäischer Orchideen : Mitteilungsblatt
des AHO Baden-Württemberg , 54(1–2), 3–27.'
bjps: 'Klüber M and Lohr M (2022) Das Kriechende Netzblatt Goodyera repens
(L.) R.Br. – Orchidee des Jahres 2021 . Journal europäischer Orchideen : Mitteilungsblatt
des AHO Baden-Württemberg 54, 3–27.'
chicago: 'Klüber, Marco, and Mathias Lohr. “Das Kriechende Netzblatt Goodyera repens
(L.) R.Br. – Orchidee des Jahres 2021 .” Journal europäischer Orchideen :
Mitteilungsblatt des AHO Baden-Württemberg 54, no. 1–2 (2022): 3–27.'
chicago-de: 'Klüber, Marco und Mathias Lohr. 2022. Das Kriechende Netzblatt Goodyera
repens (L.) R.Br. – Orchidee des Jahres 2021 . Journal europäischer Orchideen :
Mitteilungsblatt des AHO Baden-Württemberg 54, Nr. 1–2: 3–27.'
din1505-2-1: 'Klüber, Marco ; Lohr, Mathias: Das Kriechende Netzblatt
Goodyera repens (L.) R.Br. – Orchidee des Jahres 2021 . In: Journal europäischer
Orchideen : Mitteilungsblatt des AHO Baden-Württemberg Bd. 54. Stuttgart,
AHO (2022), Nr. 1–2, S. 3–27'
havard: 'M. Klüber, M. Lohr, Das Kriechende Netzblatt Goodyera repens (L.) R.Br.
– Orchidee des Jahres 2021 , Journal europäischer Orchideen : Mitteilungsblatt
des AHO Baden-Württemberg . 54 (2022) 3–27.'
ieee: 'M. Klüber and M. Lohr, “Das Kriechende Netzblatt Goodyera repens (L.) R.Br.
– Orchidee des Jahres 2021 ,” Journal europäischer Orchideen : Mitteilungsblatt
des AHO Baden-Württemberg , vol. 54, no. 1–2, pp. 3–27, 2022.'
mla: 'Klüber, Marco, and Mathias Lohr. “Das Kriechende Netzblatt Goodyera repens
(L.) R.Br. – Orchidee des Jahres 2021 .” Journal europäischer Orchideen :
Mitteilungsblatt des AHO Baden-Württemberg , vol. 54, no. 1–2, 2022, pp. 3–27.'
short: 'M. Klüber, M. Lohr, Journal europäischer Orchideen : Mitteilungsblatt des
AHO Baden-Württemberg 54 (2022) 3–27.'
ufg: 'Klüber, Marco/Lohr, Mathias: Das Kriechende Netzblatt Goodyera repens
(L.) R.Br. – Orchidee des Jahres 2021 , in: Journal europäischer Orchideen :
Mitteilungsblatt des AHO Baden-Württemberg 54 (2022), H. 1–2, S. 3–27.'
van: 'Klüber M, Lohr M. Das Kriechende Netzblatt Goodyera repens (L.) R.Br. – Orchidee
des Jahres 2021 . Journal europäischer Orchideen : Mitteilungsblatt des AHO Baden-Württemberg
. 2022;54(1–2):3–27.'
date_created: 2023-10-25T16:32:42Z
date_updated: 2024-04-02T12:38:21Z
ddc:
- '580'
department:
- _id: DEP9012
- _id: DEP1310
has_accepted_license: '1'
intvolume: ' 54'
issue: 1-2
keyword:
- Orchidaceae
- Goodyera repens
- Orchid of the Year 2021
- characteristics
- distribution
- life history
- monitoring
- threats
- conservation status
- protection
- Germany.
language:
- iso: ger
main_file_link:
- open_access: '1'
url: https://www.researchgate.net/publication/377571933_Das_Kriechende_Netzblatt_Goodyera_repens_L_R_Brown_-_Orchidee_des_Jahres_2021
oa: '1'
page: 3-27
place: Stuttgart
publication: 'Journal europäischer Orchideen : Mitteilungsblatt des AHO Baden-Württemberg '
publication_identifier:
issn:
- 0945-7909
publication_status: published
publisher: AHO
status: public
title: 'Das Kriechende Netzblatt Goodyera repens (L.) R.Br. – Orchidee des Jahres
2021 '
type: industry_journal_article
user_id: '83781'
volume: 54
year: '2022'
...
---
_id: '5419'
abstract:
- lang: eng
text: Continuous mashing provides advantages compared to conventional batch-wise
mashing in terms of space time yield. The majority of fermentable sugars are generated
during the so-called “β-amylase rest” (62–64 ◦C). These low molecular sugars are
fermented later in the brewing process by yeasts and therefore determine the beer
attenuation degree. Biological malt variations complicate the application of a
continuous system in industrial scale particularly concerning targeted quality
parameters. The aim is the prediction of sugar formation from process parameters
for a real time control system. Therefore, a semi-empirical model for sugar formation
in a continuous stirred tank reactor (CSTR) system was developed under incorporation
of the residence time distri- bution (RTD). The here presented model, which focuses
on the “β-amylase rest”, is able to predict fermentable sugar concentrations in
the continuous “β-amylase rest” with sufficient accuracy, in contrast to models
that only use the flow rate and the reactor volume to determine the reaction time.
However, the precision and trueness depend on the quality of the empirical data
acquired previously in laboratory experiments for the selected temperature and
raw material quality.
article_number: '107765'
article_type: original
author:
- first_name: Patrick
full_name: Wefing, Patrick
id: '68976'
last_name: Wefing
- first_name: Florian
full_name: Conradi, Florian
id: '68967'
last_name: Conradi
- first_name: Marc
full_name: Trilling-Haasler, Marc
id: '81622'
last_name: Trilling-Haasler
orcid: 0000-0002-3685-6383
- first_name: Peter
full_name: Neubauer, Peter
last_name: Neubauer
- first_name: Jan
full_name: Schneider, Jan
id: '13209'
last_name: Schneider
orcid: 0000-0001-6401-8873
citation:
ama: Wefing P, Conradi F, Trilling-Haasler M, Neubauer P, Schneider J. Approach
for modelling the extract formation in continuous conducted “beta-amylase rest”
as part of the production of beer mash with targeted sugar content. Biochemical
Engineering Journal . 2020;164. doi:10.1016/j.bej.2020.107765
apa: Wefing, P., Conradi, F., Trilling-Haasler, M., Neubauer, P., & Schneider,
J. (2020). Approach for modelling the extract formation in continuous conducted
“beta-amylase rest” as part of the production of beer mash with targeted sugar
content. Biochemical Engineering Journal , 164, Article 107765.
https://doi.org/10.1016/j.bej.2020.107765
bjps: Wefing P et al. (2020) Approach for Modelling the Extract Formation
in Continuous Conducted ‘Beta-Amylase Rest’ as Part of the Production of Beer
Mash with Targeted Sugar Content. Biochemical Engineering Journal 164.
chicago: Wefing, Patrick, Florian Conradi, Marc Trilling-Haasler, Peter Neubauer,
and Jan Schneider. “Approach for Modelling the Extract Formation in Continuous
Conducted ‘Beta-Amylase Rest’ as Part of the Production of Beer Mash with Targeted
Sugar Content.” Biochemical Engineering Journal 164 (2020). https://doi.org/10.1016/j.bej.2020.107765.
chicago-de: Wefing, Patrick, Florian Conradi, Marc Trilling-Haasler, Peter Neubauer
und Jan Schneider. 2020. Approach for modelling the extract formation in continuous
conducted „beta-amylase rest“ as part of the production of beer mash with targeted
sugar content. Biochemical Engineering Journal 164. doi:10.1016/j.bej.2020.107765,
.
din1505-2-1: 'Wefing, Patrick ; Conradi, Florian ; Trilling-Haasler,
Marc ; Neubauer, Peter ;
Schneider, Jan: Approach for modelling
the extract formation in continuous conducted „beta-amylase rest“ as part of the
production of beer mash with targeted sugar content. In: Biochemical Engineering
Journal Bd. 164 (2020)'
havard: P. Wefing, F. Conradi, M. Trilling-Haasler, P. Neubauer, J. Schneider, Approach
for modelling the extract formation in continuous conducted “beta-amylase rest”
as part of the production of beer mash with targeted sugar content, Biochemical
Engineering Journal . 164 (2020).
ieee: 'P. Wefing, F. Conradi, M. Trilling-Haasler, P. Neubauer, and J. Schneider,
“Approach for modelling the extract formation in continuous conducted ‘beta-amylase
rest’ as part of the production of beer mash with targeted sugar content,” Biochemical
Engineering Journal , vol. 164, Art. no. 107765, 2020, doi: 10.1016/j.bej.2020.107765.'
mla: Wefing, Patrick, et al. “Approach for Modelling the Extract Formation in Continuous
Conducted ‘Beta-Amylase Rest’ as Part of the Production of Beer Mash with Targeted
Sugar Content.” Biochemical Engineering Journal , vol. 164, 107765, 2020,
https://doi.org/10.1016/j.bej.2020.107765.
short: P. Wefing, F. Conradi, M. Trilling-Haasler, P. Neubauer, J. Schneider, Biochemical
Engineering Journal 164 (2020).
ufg: 'Wefing, Patrick u. a.: Approach for modelling the extract formation
in continuous conducted „beta-amylase rest“ as part of the production of beer
mash with targeted sugar content, in: Biochemical Engineering Journal
164 (2020).'
van: Wefing P, Conradi F, Trilling-Haasler M, Neubauer P, Schneider J. Approach
for modelling the extract formation in continuous conducted “beta-amylase rest”
as part of the production of beer mash with targeted sugar content. Biochemical
Engineering Journal . 2020;164.
date_created: 2021-04-08T05:59:08Z
date_updated: 2024-07-03T07:08:55Z
department:
- _id: DEP4023
- _id: DEP1308
- _id: DEP4018
doi: 10.1016/j.bej.2020.107765
intvolume: ' 164'
keyword:
- Continuous mashing
- Residence time distribution
- Beer
- Enzyme bioreactor
- Maltose rest
language:
- iso: eng
publication: 'Biochemical Engineering Journal '
publication_status: published
quality_controlled: '1'
status: public
title: Approach for modelling the extract formation in continuous conducted "beta-amylase
rest" as part of the production of beer mash with targeted sugar content
type: journal_article
user_id: '83780'
volume: 164
year: '2020'
...
---
_id: '2008'
abstract:
- lang: eng
text: "We concentrate our research activities on the multivariate feature selection,
which is one important part of many machine learning tasks. In partucular, Linear
Discriminant Analysis [1] belongs to the state-of-the-art methods for the multivariate
analysis. From the theoretical point of view, it is the well-known fact that LDA
is best suitable in the case the features are Gaussian distributed.\r\nIn the
theoretical part of the presented paper, we analyse the properties of the multivariate
discriminant analysis with respect to the feature selection. In this context,
we consider a binary supervised learning task and assume that the features are
Gaussian distributed. The discriminant analysis solves the mentioned supervised
learning task by maximising of the discriminant value, calculated for the linear
combination of the features.\r\nThe initial LDA solution a 2 Rd is considered
for all given features from the feature space X \x1A Rd. The corresponding discriminant
is calculated by the formula:\r\nd(a; x1, . . . , xd) := (μ+ − μ−)2\r\n\e2+\r\n+
\e2−\r\n,\r\nwhere μ+/− are projected class means and \e2 +/− are projected class
variances (with respect to a). We proof several propositions with the aim to find
subsets of the features having higher discriminant value as original d(a; x1,
. . . , xd). For the suitability in the real world settings, here we are interested
in fast searching for such subsets.\r\nThe performance of the mentioned propositions
is examined experimentally on datasets from UCI repository [2]. Several application
scenarien will be discussed and tested on the datasets. In addition, tests show
that the performance can be achieved also in the case the features are not Gaussian
distributed."
author:
- first_name: Helene
full_name: Dörksen, Helene
id: '46416'
last_name: Dörksen
- first_name: Volker
full_name: Lohweg, Volker
id: '1804'
last_name: Lohweg
orcid: 0000-0002-3325-7887
citation:
ama: 'Dörksen H, Lohweg V. Multivariate Gaussian Feature Selection. . In: European
Conference on Data Analysis (ECDA2018). Paderborn, Germany; 2018.'
apa: Dörksen, H., & Lohweg, V. (2018). Multivariate Gaussian Feature Selection.
. In European Conference on Data Analysis (ECDA2018). Paderborn, Germany.
bjps: Dörksen H and Lohweg V (2018) Multivariate Gaussian Feature Selection.
. European Conference on Data Analysis (ECDA2018). Paderborn, Germany.
chicago: Dörksen, Helene, and Volker Lohweg. “Multivariate Gaussian Feature Selection.
.” In European Conference on Data Analysis (ECDA2018). Paderborn, Germany,
2018.
chicago-de: 'Dörksen, Helene und Volker Lohweg. 2018. Multivariate Gaussian Feature
Selection. . In: European Conference on Data Analysis (ECDA2018). Paderborn,
Germany.'
din1505-2-1: 'Dörksen, Helene ; Lohweg, Volker: Multivariate Gaussian
Feature Selection. . In: European Conference on Data Analysis (ECDA2018).
Paderborn, Germany, 2018'
havard: 'H. Dörksen, V. Lohweg, Multivariate Gaussian Feature Selection. , in: European
Conference on Data Analysis (ECDA2018), Paderborn, Germany, 2018.'
ieee: H. Dörksen and V. Lohweg, “Multivariate Gaussian Feature Selection. ,” in
European Conference on Data Analysis (ECDA2018), Paderborn, 2018.
mla: Dörksen, Helene, and Volker Lohweg. “Multivariate Gaussian Feature Selection.
.” European Conference on Data Analysis (ECDA2018), 2018.
short: 'H. Dörksen, V. Lohweg, in: European Conference on Data Analysis (ECDA2018),
Paderborn, Germany, 2018.'
ufg: 'Dörksen, Helene/Lohweg, Volker (2018): Multivariate Gaussian Feature
Selection. , in: European Conference on Data Analysis (ECDA2018), Paderborn,
Germany.'
van: 'Dörksen H, Lohweg V. Multivariate Gaussian Feature Selection. . In: European
Conference on Data Analysis (ECDA2018). Paderborn, Germany; 2018.'
conference:
end_date: 2018-07-06
location: Paderborn
name: European Conference on Data Analysis
start_date: 2018-07-04
date_created: 2019-11-25T08:35:48Z
date_updated: 2023-03-15T13:49:38Z
department:
- _id: DEP5023
keyword:
- multivariate feature selection
- Gaussian distribution
- linear discriminant analysis
language:
- iso: eng
place: Paderborn, Germany
publication: European Conference on Data Analysis (ECDA2018)
status: public
title: 'Multivariate Gaussian Feature Selection. '
type: conference
user_id: '15514'
year: 2018
...
---
_id: '2088'
abstract:
- lang: eng
text: "Clustering remains a major topic in machine learning; it is used e.g. for
document categorization, for data mining, and for image analysis. In all these
application areas, clustering algorithms try to identify groups of related data
in large data sets.\r\n\r\nIn this paper, the established clustering algorithm
MajorClust ([12]) is improved; making it applicable to data sets with few structure
on the local scale—so called near-homogeneous graphs. This new algorithm MCProb
is verified empirically using the problem of image clustering. Furthermore, MCProb
is analyzed theoretically. For the applications examined so-far, MCProb outperforms
other established clustering techniques."
author:
- first_name: Oliver
full_name: Niggemann, Oliver
id: '10876'
last_name: Niggemann
- first_name: Volker
full_name: Lohweg, Volker
id: '1804'
last_name: Lohweg
orcid: 0000-0002-3325-7887
- first_name: Tim
full_name: Tack, Tim
last_name: Tack
citation:
ama: 'Niggemann O, Lohweg V, Tack T. A Probabilistic MajorClust Variant for the
Clustering of Near-Homogeneous Graphs. In: 33rd Annual German Conference on
Artificial Intelligence (KI 2010). Vol 6359. Lecture Notes in Computer Science.
Berlin: Springer; 2010:184-194. doi:https://doi.org/10.1007/978-3-642-16111-7_21'
apa: 'Niggemann, O., Lohweg, V., & Tack, T. (2010). A Probabilistic MajorClust
Variant for the Clustering of Near-Homogeneous Graphs. In 33rd Annual German
Conference on Artificial Intelligence (KI 2010) (Vol. 6359, pp. 184–194).
Berlin: Springer. https://doi.org/10.1007/978-3-642-16111-7_21'
bjps: 'Niggemann O, Lohweg V and Tack T (2010) A Probabilistic MajorClust
Variant for the Clustering of Near-Homogeneous Graphs. 33rd Annual German Conference
on Artificial Intelligence (KI 2010), vol. 6359. Berlin: Springer, pp. 184–194.'
chicago: 'Niggemann, Oliver, Volker Lohweg, and Tim Tack. “A Probabilistic MajorClust
Variant for the Clustering of Near-Homogeneous Graphs.” In 33rd Annual German
Conference on Artificial Intelligence (KI 2010), 6359:184–94. Lecture Notes
in Computer Science. Berlin: Springer, 2010. https://doi.org/10.1007/978-3-642-16111-7_21.'
chicago-de: 'Niggemann, Oliver, Volker Lohweg und Tim Tack. 2010. A Probabilistic
MajorClust Variant for the Clustering of Near-Homogeneous Graphs. In: 33rd
Annual German Conference on Artificial Intelligence (KI 2010), 6359:184–194.
Lecture Notes in Computer Science. Berlin: Springer. doi:https://doi.org/10.1007/978-3-642-16111-7_21,
.'
din1505-2-1: 'Niggemann, Oliver ;
Lohweg, Volker ; Tack,
Tim: A Probabilistic MajorClust Variant for the Clustering of Near-Homogeneous
Graphs. In: 33rd Annual German Conference on Artificial Intelligence (KI 2010),
Lecture Notes in Computer Science. Bd. 6359. Berlin : Springer, 2010, S. 184–194'
havard: 'O. Niggemann, V. Lohweg, T. Tack, A Probabilistic MajorClust Variant for
the Clustering of Near-Homogeneous Graphs, in: 33rd Annual German Conference on
Artificial Intelligence (KI 2010), Springer, Berlin, 2010: pp. 184–194.'
ieee: O. Niggemann, V. Lohweg, and T. Tack, “A Probabilistic MajorClust Variant
for the Clustering of Near-Homogeneous Graphs,” in 33rd Annual German Conference
on Artificial Intelligence (KI 2010), 2010, vol. 6359, pp. 184–194.
mla: Niggemann, Oliver, et al. “A Probabilistic MajorClust Variant for the Clustering
of Near-Homogeneous Graphs.” 33rd Annual German Conference on Artificial Intelligence
(KI 2010), vol. 6359, Springer, 2010, pp. 184–94, doi:https://doi.org/10.1007/978-3-642-16111-7_21.
short: 'O. Niggemann, V. Lohweg, T. Tack, in: 33rd Annual German Conference on Artificial
Intelligence (KI 2010), Springer, Berlin, 2010, pp. 184–194.'
ufg: 'Niggemann, Oliver et. al. (2010): A Probabilistic MajorClust Variant
for the Clustering of Near-Homogeneous Graphs, in: 33rd Annual German Conference
on Artificial Intelligence (KI 2010) (=Lecture Notes in Computer Science
6359), Berlin, S. 184–194.'
van: 'Niggemann O, Lohweg V, Tack T. A Probabilistic MajorClust Variant for the
Clustering of Near-Homogeneous Graphs. In: 33rd Annual German Conference on Artificial
Intelligence (KI 2010). Berlin: Springer; 2010. p. 184–94. (Lecture Notes in Computer
Science; vol. 6359).'
date_created: 2019-12-02T08:21:26Z
date_updated: 2023-03-15T13:49:38Z
department:
- _id: DEP5023
doi: https://doi.org/10.1007/978-3-642-16111-7_21
intvolume: ' 6359'
keyword:
- Markov Chain
- Cluster Algorithm
- Edge Weight
- Spectral Cluster
- Stable Distribution
language:
- iso: eng
main_file_link:
- url: https://link.springer.com/chapter/10.1007/978-3-642-16111-7_21
page: 184-194
place: Berlin
publication: 33rd Annual German Conference on Artificial Intelligence (KI 2010)
publication_identifier:
eisbn:
- 978-3-642-16111-7
isbn:
- 978-3-642-16110-0
publication_status: published
publisher: Springer
series_title: Lecture Notes in Computer Science
status: public
title: A Probabilistic MajorClust Variant for the Clustering of Near-Homogeneous Graphs
type: conference
user_id: '45673'
volume: 6359
year: 2010
...